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class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" alt="Palash Panja" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/46171107/13126679/14439614/s200_palash.panja.jpg" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Palash Panja</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://utah.academia.edu/">University of Utah</a>, <a class="u-tcGrayDarker" href="https://utah.academia.edu/Departments/Energy_Geoscience_Institute/Documents">Energy & Geoscience Institute</a>, <span class="u-tcGrayDarker">Research Scientist</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" 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id="Papers"><h3 class="profile--tab_heading_container">Papers by Palash Panja</h3></div><div class="js-work-strip profile--work_container" data-work-id="93349087"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/93349087/Injection_of_Flue_Gas_Improves_CO2_Permeability_and_Storage_Capacity_in_Coal_A_Promising_Technology"><img alt="Research paper thumbnail of Injection of Flue Gas Improves CO2 Permeability and Storage Capacity in Coal: A Promising Technology" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/93349087/Injection_of_Flue_Gas_Improves_CO2_Permeability_and_Storage_Capacity_in_Coal_A_Promising_Technology">Injection of Flue Gas Improves CO2 Permeability and Storage Capacity in Coal: A Promising Technology</a></div><div class="wp-workCard_item"><span>Day 2 Tue, October 04, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Swelling of coal thus reducing permeability is the main detrimental for any carbon dioxide (CO2) ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Swelling of coal thus reducing permeability is the main detrimental for any carbon dioxide (CO2) capture and storage (CCS) projects. Additionally, CO2 capture from flue gas or direct air is an expensive process. The current commercial simulators are impaired of combining various effects such as fluid segregation, adsorption, Darcy&#39;s flow, and permeability change in coal. The objective of this study is to develop a numerical model to simulate flue gas injection in coal. The study is motivated by encouraging preliminary results from lab-scale experiments of injection of flue gas (ideally a mixture of Nitrogen and CO2) in coal. Bench-scale experiments demonstrated the swelling reduction caused by the selective flow of surrogate flue gas N2-CO2 mixture, based on the fluid stratification at sub-critical conditions where the density of pure components in a vertical container causes stratification as predicted from the Grashof number and the thermodynamic properties of fluids. The nume...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="93349087"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="93349087"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 93349087; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=93349087]").text(description); $(".js-view-count[data-work-id=93349087]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 93349087; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='93349087']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 93349087, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=93349087]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":93349087,"title":"Injection of Flue Gas Improves CO2 Permeability and Storage Capacity in Coal: A Promising Technology","translated_title":"","metadata":{"abstract":"Swelling of coal thus reducing permeability is the main detrimental for any carbon dioxide (CO2) capture and storage (CCS) projects. 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Bench-scale experiments demonstrated the swelling reduction caused by the selective flow of surrogate flue gas N2-CO2 mixture, based on the fluid stratification at sub-critical conditions where the density of pure components in a vertical container causes stratification as predicted from the Grashof number and the thermodynamic properties of fluids. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772861"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772861/Data_Processing_Protocol_for_Regression_of_Geothermal_Times_Series_with_Uneven_Intervals"><img alt="Research paper thumbnail of Data Processing Protocol for Regression of Geothermal Times Series with Uneven Intervals" class="work-thumbnail" src="https://attachments.academia-assets.com/85049205/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772861/Data_Processing_Protocol_for_Regression_of_Geothermal_Times_Series_with_Uneven_Intervals">Data Processing Protocol for Regression of Geothermal Times Series with Uneven Intervals</a></div><div class="wp-workCard_item"><span>arXiv: Data Analysis, Statistics and Probability</span><span>, May 16, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e69960e27b99ec3ad2a3095a70794d0f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85049205,"asset_id":77772861,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85049205/download_file?st=MTczMjc5NzE0OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772861"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772861"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772861; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772855"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772855/Enhanced_Recovery_in_Shales_Molecular_Investigation_of_CO2_Energized_Fluid_for_Re_Fracturing_Shale_Formations"><img alt="Research paper thumbnail of Enhanced Recovery in Shales: Molecular Investigation of CO2 Energized Fluid for Re-Fracturing Shale Formations" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772855/Enhanced_Recovery_in_Shales_Molecular_Investigation_of_CO2_Energized_Fluid_for_Re_Fracturing_Shale_Formations">Enhanced Recovery in Shales: Molecular Investigation of CO2 Energized Fluid for Re-Fracturing Shale Formations</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The liquid and gas rich shales are low permeability, low porosity but high organic content reserv...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The liquid and gas rich shales are low permeability, low porosity but high organic content reservoirs. They need effective sub-surface, in-situ stimulation for economic production. Sometimes due to ineffective initial completion, the shale wells don&amp;#39;t produce well. An effective re-stimulation or refracturing can shoot up the production from these mature low producing wells. The fracture fluid plays a key role in determining the fate of such reservoirs. Typically, slickwater or gelled water is used as a fracture-fluid. However, the energized fluids, energized with carbon dioxide (CO 2) has shown to increase the performance. They reduce the water volumes, problems associated with water usage and have superior proppant transport capabilities. The Molecular Dynamics (MD) Simulations technique is used in the current work to understand the interaction between carbon dioxide and hydrocarbons rich Type II kerogen in the shale rocks. The Nose-Hoover style non-Hamiltonian equations of motion are used in a molecular simulator to generate positions and velocities of carbon dioxide and kerogen molecules sampled from the canonical (nvt) and isothermal-isobaric (npt) ensembles. In this work, we propose that carbon dioxide energized fluids be used for re-fracturing the low producing shale formations. In shale fractured with carbon dioxide enhanced fracture fluid, some of the CO 2 is retained in the formation and doesn&amp;#39;t flow back after the fracturing operation. The current work study the fate of the retained portion of CO 2 in an energized fracturing operation. MD simulations reveal that carbon dioxide has more affinity than methane and heavier hydrocarbons like octane, to be retained in the organic part known as kerogen (Pathak M., 2015a) found in shales. MD simulations also reveal that the kerogen shrinks as a results of absorption of CO2 which leads to effective decrease in the skin of the formation. This helps in better fluid flow between the formation and fractures. The carbon dioxide dissolved in the kerogen helps to displace hydrocarbons absorbed in the kerogen. The diffusion coefficient of carbon dioxide in kerogen is found to be of an order of magnitude less than methane or octane. MD simulations technique has been used for the first time to explore the interaction between carbon dioxide and organic matter in shales at the molecular scale. The mechanism of the carbon dioxide energized fracture-fluid induced enhanced recovery is understood to understand the key processes that helps in taking decisions of a re-fracture job.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772855"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772855"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772855; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772855]").text(description); $(".js-view-count[data-work-id=77772855]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772855; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772855']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772855, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772855]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772855,"title":"Enhanced Recovery in Shales: Molecular Investigation of CO2 Energized Fluid for Re-Fracturing Shale Formations","translated_title":"","metadata":{"abstract":"The liquid and gas rich shales are low permeability, low porosity but high organic content reservoirs. They need effective sub-surface, in-situ stimulation for economic production. Sometimes due to ineffective initial completion, the shale wells don\u0026amp;#39;t produce well. An effective re-stimulation or refracturing can shoot up the production from these mature low producing wells. The fracture fluid plays a key role in determining the fate of such reservoirs. Typically, slickwater or gelled water is used as a fracture-fluid. However, the energized fluids, energized with carbon dioxide (CO 2) has shown to increase the performance. They reduce the water volumes, problems associated with water usage and have superior proppant transport capabilities. The Molecular Dynamics (MD) Simulations technique is used in the current work to understand the interaction between carbon dioxide and hydrocarbons rich Type II kerogen in the shale rocks. The Nose-Hoover style non-Hamiltonian equations of motion are used in a molecular simulator to generate positions and velocities of carbon dioxide and kerogen molecules sampled from the canonical (nvt) and isothermal-isobaric (npt) ensembles. In this work, we propose that carbon dioxide energized fluids be used for re-fracturing the low producing shale formations. In shale fractured with carbon dioxide enhanced fracture fluid, some of the CO 2 is retained in the formation and doesn\u0026amp;#39;t flow back after the fracturing operation. The current work study the fate of the retained portion of CO 2 in an energized fracturing operation. MD simulations reveal that carbon dioxide has more affinity than methane and heavier hydrocarbons like octane, to be retained in the organic part known as kerogen (Pathak M., 2015a) found in shales. MD simulations also reveal that the kerogen shrinks as a results of absorption of CO2 which leads to effective decrease in the skin of the formation. This helps in better fluid flow between the formation and fractures. The carbon dioxide dissolved in the kerogen helps to displace hydrocarbons absorbed in the kerogen. The diffusion coefficient of carbon dioxide in kerogen is found to be of an order of magnitude less than methane or octane. MD simulations technique has been used for the first time to explore the interaction between carbon dioxide and organic matter in shales at the molecular scale. The mechanism of the carbon dioxide energized fracture-fluid induced enhanced recovery is understood to understand the key processes that helps in taking decisions of a re-fracture job.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}}},"translated_abstract":"The liquid and gas rich shales are low permeability, low porosity but high organic content reservoirs. They need effective sub-surface, in-situ stimulation for economic production. 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The Nose-Hoover style non-Hamiltonian equations of motion are used in a molecular simulator to generate positions and velocities of carbon dioxide and kerogen molecules sampled from the canonical (nvt) and isothermal-isobaric (npt) ensembles. In this work, we propose that carbon dioxide energized fluids be used for re-fracturing the low producing shale formations. In shale fractured with carbon dioxide enhanced fracture fluid, some of the CO 2 is retained in the formation and doesn\u0026amp;#39;t flow back after the fracturing operation. The current work study the fate of the retained portion of CO 2 in an energized fracturing operation. MD simulations reveal that carbon dioxide has more affinity than methane and heavier hydrocarbons like octane, to be retained in the organic part known as kerogen (Pathak M., 2015a) found in shales. MD simulations also reveal that the kerogen shrinks as a results of absorption of CO2 which leads to effective decrease in the skin of the formation. This helps in better fluid flow between the formation and fractures. The carbon dioxide dissolved in the kerogen helps to displace hydrocarbons absorbed in the kerogen. The diffusion coefficient of carbon dioxide in kerogen is found to be of an order of magnitude less than methane or octane. MD simulations technique has been used for the first time to explore the interaction between carbon dioxide and organic matter in shales at the molecular scale. The mechanism of the carbon dioxide energized fracture-fluid induced enhanced recovery is understood to understand the key processes that helps in taking decisions of a re-fracture job.","internal_url":"https://www.academia.edu/77772855/Enhanced_Recovery_in_Shales_Molecular_Investigation_of_CO2_Energized_Fluid_for_Re_Fracturing_Shale_Formations","translated_internal_url":"","created_at":"2022-04-27T05:00:27.878-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Enhanced_Recovery_in_Shales_Molecular_Investigation_of_CO2_Energized_Fluid_for_Re_Fracturing_Shale_Formations","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":29056,"name":"Hydraulic Fracturing","url":"https://www.academia.edu/Documents/in/Hydraulic_Fracturing"},{"id":33451,"name":"Oil Shale","url":"https://www.academia.edu/Documents/in/Oil_Shale"},{"id":333293,"name":"Shale gas","url":"https://www.academia.edu/Documents/in/Shale_gas"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772853"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772853/Production_of_Liquid_Hydrocarbons_from_Shales"><img alt="Research paper thumbnail of Production of Liquid Hydrocarbons from Shales" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772853/Production_of_Liquid_Hydrocarbons_from_Shales">Production of Liquid Hydrocarbons from Shales</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Shale and mudstone are sedimentary rocks composed of clay-sized particles. These clayey rocks are...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Shale and mudstone are sedimentary rocks composed of clay-sized particles. These clayey rocks are excellent candidates for petroleum source rocks because they are relatively rich in organic matter (kerogen) dispersed in the rock pore space. Kerogen is thermally cracked to oil and gas at depths under temperatures of 60–150 C over geologic time scales (millions of years). Some of the generated oil and gas migrates to porous reservoirs enclosed by traps where they create conventional prospects. However, a considerable amount of hydrocarbon remains within the source rock, thus making shale and mudstone a source rock as well as a reservoir. Liquid hydrocarbons refer to light and heavy crude oils and condensates. Liquid condensate is obtained from wet gas and gas-condensate reservoirs. Appropriate drilling and stimulation technologies are essential to achieve economic rates of production from shales due to the ultralow permeabilities and very low porosities found in these rocks. Tight shale reservoirs are complex and pose many challenges to petroleum production. Some of these challenges revolve around well placement, hydraulic fracture spacing, drilling operations and scheduling optimization, environmental impact minimization, etc. while following strict regulations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772853"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772853"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772853; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772853]").text(description); $(".js-view-count[data-work-id=77772853]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772853; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772853']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772853, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772853]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772853,"title":"Production of Liquid Hydrocarbons from Shales","translated_title":"","metadata":{"abstract":"Shale and mudstone are sedimentary rocks composed of clay-sized particles. These clayey rocks are excellent candidates for petroleum source rocks because they are relatively rich in organic matter (kerogen) dispersed in the rock pore space. Kerogen is thermally cracked to oil and gas at depths under temperatures of 60–150 \u0001C over geologic time scales (millions of years). Some of the generated oil and gas migrates to porous reservoirs enclosed by traps where they create conventional prospects. However, a considerable amount of hydrocarbon remains within the source rock, thus making shale and mudstone a source rock as well as a reservoir. Liquid hydrocarbons refer to light and heavy crude oils and condensates. Liquid condensate is obtained from wet gas and gas-condensate reservoirs. Appropriate drilling and stimulation technologies are essential to achieve economic rates of production from shales due to the ultralow permeabilities and very low porosities found in these rocks. Tight shale reservoirs are complex and pose many challenges to petroleum production. Some of these challenges revolve around well placement, hydraulic fracture spacing, drilling operations and scheduling optimization, environmental impact minimization, etc. while following strict regulations.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}}},"translated_abstract":"Shale and mudstone are sedimentary rocks composed of clay-sized particles. These clayey rocks are excellent candidates for petroleum source rocks because they are relatively rich in organic matter (kerogen) dispersed in the rock pore space. Kerogen is thermally cracked to oil and gas at depths under temperatures of 60–150 \u0001C over geologic time scales (millions of years). Some of the generated oil and gas migrates to porous reservoirs enclosed by traps where they create conventional prospects. However, a considerable amount of hydrocarbon remains within the source rock, thus making shale and mudstone a source rock as well as a reservoir. Liquid hydrocarbons refer to light and heavy crude oils and condensates. Liquid condensate is obtained from wet gas and gas-condensate reservoirs. Appropriate drilling and stimulation technologies are essential to achieve economic rates of production from shales due to the ultralow permeabilities and very low porosities found in these rocks. Tight shale reservoirs are complex and pose many challenges to petroleum production. Some of these challenges revolve around well placement, hydraulic fracture spacing, drilling operations and scheduling optimization, environmental impact minimization, etc. while following strict regulations.","internal_url":"https://www.academia.edu/77772853/Production_of_Liquid_Hydrocarbons_from_Shales","translated_internal_url":"","created_at":"2022-04-27T05:00:27.782-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Production_of_Liquid_Hydrocarbons_from_Shales","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":33451,"name":"Oil Shale","url":"https://www.academia.edu/Documents/in/Oil_Shale"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772851"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772851/What_Happens_to_Permeability_at_the_Nanoscale_A_Molecular_Dynamics_Simulation_Study"><img alt="Research paper thumbnail of What Happens to Permeability at the Nanoscale? A Molecular Dynamics Simulation Study" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772851/What_Happens_to_Permeability_at_the_Nanoscale_A_Molecular_Dynamics_Simulation_Study">What Happens to Permeability at the Nanoscale? A Molecular Dynamics Simulation Study</a></div><div class="wp-workCard_item"><span>Proceedings of the 5th Unconventional Resources Technology Conference</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">As one of the industry&amp;#39;s corner stones, the concept of permeability is used anywhere from...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">As one of the industry&amp;#39;s corner stones, the concept of permeability is used anywhere from reservoir characterization to full-fledged reservoir simulation models. Permeability is defined as a rock property that describes the ease of fluid flow, implying that permeability is fundamentally independent of fluid. This concept has worked successfully for conventional reservoirs and is now well accepted in the industry. However, as pore dimensions approach the nanoscale in unconventional reservoirs, we must re-evaluate the validity of a fluid-independent permeability concept. Does our conventional understanding of permeability break as we reach nanoscale pores? If so, how do rock-fluid interactions affect fluid flow and permeability calculations? We approximate the porous medium as a collection of long cylinders with and without wall roughness. The permeability from this pseudo-porous system is determined as a function of pore throat diameter using Darcy&amp;#39;s law and the Hagen-Poiseuille equation. The result is a permeability versus pore throat diameter relationship based on continuum mechanics with a no-slip boundary condition at the rock walls. To investigate the effects of rock-fluid interactions on permeability, we use molecular dynamics to simulate the pseudo-porous geometry with carbon nanotubes flowing water, hexane, and a mixture. Initially, we observe that continuum permeability and molecular simulation permeability converge for all fluids; however, as pore throat diameter shrinks into the nanoscale, continuum and molecular permeabilities deviate significantly. The extent at which deviations occur depends on the type of fluid, pore throat diameter, and whether the carbon nanotube is hydrophobic or hydrophilic. In addition, the effects of rock-fluid interactions also affect multiphase behavior resulting in different relative permeability curves depending on the pore throat diameter. Recently, research has been aimed towards the study of phase behavior and transport properties of numerous fluids inside carbon nanotubes. In this work, we study and quantify the effects of rock-fluid interactions on permeability and its consequences on liquid multiphase flow when pore throats reach nanoscale dimensions. The implications of a fluid-dependent permeability concept at the nanoscale have enormous interdisciplinary ramifications and could lead to a better understanding of unconventional reservoirs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772851"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772851"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772851; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772851]").text(description); $(".js-view-count[data-work-id=77772851]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772851; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772851']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772851, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772851]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772851,"title":"What Happens to Permeability at the Nanoscale? A Molecular Dynamics Simulation Study","translated_title":"","metadata":{"abstract":"As one of the industry\u0026amp;#39;s corner stones, the concept of permeability is used anywhere from reservoir characterization to full-fledged reservoir simulation models. Permeability is defined as a rock property that describes the ease of fluid flow, implying that permeability is fundamentally independent of fluid. This concept has worked successfully for conventional reservoirs and is now well accepted in the industry. However, as pore dimensions approach the nanoscale in unconventional reservoirs, we must re-evaluate the validity of a fluid-independent permeability concept. Does our conventional understanding of permeability break as we reach nanoscale pores? If so, how do rock-fluid interactions affect fluid flow and permeability calculations? We approximate the porous medium as a collection of long cylinders with and without wall roughness. The permeability from this pseudo-porous system is determined as a function of pore throat diameter using Darcy\u0026amp;#39;s law and the Hagen-Poiseuille equation. The result is a permeability versus pore throat diameter relationship based on continuum mechanics with a no-slip boundary condition at the rock walls. To investigate the effects of rock-fluid interactions on permeability, we use molecular dynamics to simulate the pseudo-porous geometry with carbon nanotubes flowing water, hexane, and a mixture. Initially, we observe that continuum permeability and molecular simulation permeability converge for all fluids; however, as pore throat diameter shrinks into the nanoscale, continuum and molecular permeabilities deviate significantly. The extent at which deviations occur depends on the type of fluid, pore throat diameter, and whether the carbon nanotube is hydrophobic or hydrophilic. In addition, the effects of rock-fluid interactions also affect multiphase behavior resulting in different relative permeability curves depending on the pore throat diameter. Recently, research has been aimed towards the study of phase behavior and transport properties of numerous fluids inside carbon nanotubes. In this work, we study and quantify the effects of rock-fluid interactions on permeability and its consequences on liquid multiphase flow when pore throats reach nanoscale dimensions. The implications of a fluid-dependent permeability concept at the nanoscale have enormous interdisciplinary ramifications and could lead to a better understanding of unconventional reservoirs.","publisher":"American Association of Petroleum Geologists","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Proceedings of the 5th Unconventional Resources Technology Conference"},"translated_abstract":"As one of the industry\u0026amp;#39;s corner stones, the concept of permeability is used anywhere from reservoir characterization to full-fledged reservoir simulation models. Permeability is defined as a rock property that describes the ease of fluid flow, implying that permeability is fundamentally independent of fluid. This concept has worked successfully for conventional reservoirs and is now well accepted in the industry. However, as pore dimensions approach the nanoscale in unconventional reservoirs, we must re-evaluate the validity of a fluid-independent permeability concept. Does our conventional understanding of permeability break as we reach nanoscale pores? If so, how do rock-fluid interactions affect fluid flow and permeability calculations? We approximate the porous medium as a collection of long cylinders with and without wall roughness. The permeability from this pseudo-porous system is determined as a function of pore throat diameter using Darcy\u0026amp;#39;s law and the Hagen-Poiseuille equation. The result is a permeability versus pore throat diameter relationship based on continuum mechanics with a no-slip boundary condition at the rock walls. To investigate the effects of rock-fluid interactions on permeability, we use molecular dynamics to simulate the pseudo-porous geometry with carbon nanotubes flowing water, hexane, and a mixture. Initially, we observe that continuum permeability and molecular simulation permeability converge for all fluids; however, as pore throat diameter shrinks into the nanoscale, continuum and molecular permeabilities deviate significantly. The extent at which deviations occur depends on the type of fluid, pore throat diameter, and whether the carbon nanotube is hydrophobic or hydrophilic. In addition, the effects of rock-fluid interactions also affect multiphase behavior resulting in different relative permeability curves depending on the pore throat diameter. Recently, research has been aimed towards the study of phase behavior and transport properties of numerous fluids inside carbon nanotubes. In this work, we study and quantify the effects of rock-fluid interactions on permeability and its consequences on liquid multiphase flow when pore throats reach nanoscale dimensions. The implications of a fluid-dependent permeability concept at the nanoscale have enormous interdisciplinary ramifications and could lead to a better understanding of unconventional reservoirs.","internal_url":"https://www.academia.edu/77772851/What_Happens_to_Permeability_at_the_Nanoscale_A_Molecular_Dynamics_Simulation_Study","translated_internal_url":"","created_at":"2022-04-27T05:00:27.684-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"What_Happens_to_Permeability_at_the_Nanoscale_A_Molecular_Dynamics_Simulation_Study","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":2736,"name":"Molecular Dynamics Simulation","url":"https://www.academia.edu/Documents/in/Molecular_Dynamics_Simulation"},{"id":62537,"name":"Porosity and Permeability in Reservoirs","url":"https://www.academia.edu/Documents/in/Porosity_and_Permeability_in_Reservoirs"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772850"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772850/Confinement_Effect_on_Porosity_and_Permeability_of_Shales"><img alt="Research paper thumbnail of Confinement Effect on Porosity and Permeability of Shales" class="work-thumbnail" src="https://attachments.academia-assets.com/85049200/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772850/Confinement_Effect_on_Porosity_and_Permeability_of_Shales">Confinement Effect on Porosity and Permeability of Shales</a></div><div class="wp-workCard_item"><span>Scientific Reports</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Porosity and permeability are the key factors in assessing the hydrocarbon productivity of unconv...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Porosity and permeability are the key factors in assessing the hydrocarbon productivity of unconventional (shale) reservoirs, which are complex in nature due to their heterogeneous mineralogy and poorly connected nano- and micro-pore systems. Experimental efforts to measure these petrophysical properties posse many limitations, because they often take weeks to complete and are difficult to reproduce. Alternatively, numerical simulations can be conducted in digital rock 3D models reconstructed from image datasets acquired via e.g., nanoscale-resolution focused ion beam–scanning electron microscopy (FIB-SEM) nano-tomography. In this study, impact of reservoir confinement (stress) on porosity and permeability of shales was investigated using two digital rock 3D models, which represented nanoporous organic/mineral microstructure of the Marcellus Shale. Five stress scenarios were simulated for different depths (2,000–6,000 feet) within the production interval of a typical oil/gas reservo...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="69cd23b2e21032b23a410a40f4fb7858" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85049200,"asset_id":77772850,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85049200/download_file?st=MTczMjc5NzE0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772850"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772850"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772850; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772850]").text(description); $(".js-view-count[data-work-id=77772850]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772850; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772850']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772850, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "69cd23b2e21032b23a410a40f4fb7858" } } $('.js-work-strip[data-work-id=77772850]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772850,"title":"Confinement Effect on Porosity and Permeability of Shales","translated_title":"","metadata":{"abstract":"Porosity and permeability are the key factors in assessing the hydrocarbon productivity of unconventional (shale) reservoirs, which are complex in nature due to their heterogeneous mineralogy and poorly connected nano- and micro-pore systems. Experimental efforts to measure these petrophysical properties posse many limitations, because they often take weeks to complete and are difficult to reproduce. Alternatively, numerical simulations can be conducted in digital rock 3D models reconstructed from image datasets acquired via e.g., nanoscale-resolution focused ion beam–scanning electron microscopy (FIB-SEM) nano-tomography. In this study, impact of reservoir confinement (stress) on porosity and permeability of shales was investigated using two digital rock 3D models, which represented nanoporous organic/mineral microstructure of the Marcellus Shale. Five stress scenarios were simulated for different depths (2,000–6,000 feet) within the production interval of a typical oil/gas reservo...","publisher":"Springer Science and Business Media LLC","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Scientific Reports"},"translated_abstract":"Porosity and permeability are the key factors in assessing the hydrocarbon productivity of unconventional (shale) reservoirs, which are complex in nature due to their heterogeneous mineralogy and poorly connected nano- and micro-pore systems. Experimental efforts to measure these petrophysical properties posse many limitations, because they often take weeks to complete and are difficult to reproduce. Alternatively, numerical simulations can be conducted in digital rock 3D models reconstructed from image datasets acquired via e.g., nanoscale-resolution focused ion beam–scanning electron microscopy (FIB-SEM) nano-tomography. In this study, impact of reservoir confinement (stress) on porosity and permeability of shales was investigated using two digital rock 3D models, which represented nanoporous organic/mineral microstructure of the Marcellus Shale. Five stress scenarios were simulated for different depths (2,000–6,000 feet) within the production interval of a typical oil/gas reservo...","internal_url":"https://www.academia.edu/77772850/Confinement_Effect_on_Porosity_and_Permeability_of_Shales","translated_internal_url":"","created_at":"2022-04-27T05:00:27.539-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":85049200,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85049200/thumbnails/1.jpg","file_name":"s41598-019-56885-y.pdf","download_url":"https://www.academia.edu/attachments/85049200/download_file?st=MTczMjc5NzE0OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Confinement_Effect_on_Porosity_and_Perme.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85049200/s41598-019-56885-y-libre.pdf?1651061713=\u0026response-content-disposition=attachment%3B+filename%3DConfinement_Effect_on_Porosity_and_Perme.pdf\u0026Expires=1732800749\u0026Signature=V3ebMdueUofOy8BSPU4xaN8XPTY31IbJiNpbfHSWUc4p9eq2gqFd93DkMx33yX0gsGgvCsM4g-meKsf~CEeGa-ZMpJgTjZzkVvemqA0137~ici9OO7fhMH14ZrymwYg8c0JTB9xbY4QYEd7qSvZlE1DXaorQa3yt-B2ztvDgOkSFuCi~xrzoV4jNID7PYAYCd~TrEzNJ4~sLl1vrjPITkW-XmhRtsuP6fRmdx2UYrf57DtrWOPcsUz5KBASoQiWfJxbq4BqtLU4~jBn~9dg2hOnUwH9VoeT~j3ljbpMBu8gKt5XqiyWI3VggmGVvL9s9KtnTidUIxK2aaRfQL0EHpQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Confinement_Effect_on_Porosity_and_Permeability_of_Shales","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[{"id":85049200,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85049200/thumbnails/1.jpg","file_name":"s41598-019-56885-y.pdf","download_url":"https://www.academia.edu/attachments/85049200/download_file?st=MTczMjc5NzE0OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Confinement_Effect_on_Porosity_and_Perme.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85049200/s41598-019-56885-y-libre.pdf?1651061713=\u0026response-content-disposition=attachment%3B+filename%3DConfinement_Effect_on_Porosity_and_Perme.pdf\u0026Expires=1732800749\u0026Signature=V3ebMdueUofOy8BSPU4xaN8XPTY31IbJiNpbfHSWUc4p9eq2gqFd93DkMx33yX0gsGgvCsM4g-meKsf~CEeGa-ZMpJgTjZzkVvemqA0137~ici9OO7fhMH14ZrymwYg8c0JTB9xbY4QYEd7qSvZlE1DXaorQa3yt-B2ztvDgOkSFuCi~xrzoV4jNID7PYAYCd~TrEzNJ4~sLl1vrjPITkW-XmhRtsuP6fRmdx2UYrf57DtrWOPcsUz5KBASoQiWfJxbq4BqtLU4~jBn~9dg2hOnUwH9VoeT~j3ljbpMBu8gKt5XqiyWI3VggmGVvL9s9KtnTidUIxK2aaRfQL0EHpQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":85049201,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85049201/thumbnails/1.jpg","file_name":"s41598-019-56885-y.pdf","download_url":"https://www.academia.edu/attachments/85049201/download_file","bulk_download_file_name":"Confinement_Effect_on_Porosity_and_Perme.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85049201/s41598-019-56885-y-libre.pdf?1651061712=\u0026response-content-disposition=attachment%3B+filename%3DConfinement_Effect_on_Porosity_and_Perme.pdf\u0026Expires=1732800749\u0026Signature=A0P~0e~KNpCsgol4fVdAqJDVKBT-1Ooa3x8ojrcrkdyuzav6pcWOmuLQhCVYaJ6r8tCInZ6QDfQyPKx-Yf-e-C9wlaEUIk5s2ImWaq2bZw-SEeLkZMAiIJXQMA-UioIeH7V02Ri1aV1qQgpRURcK6o9czVT2RpjG~u1zb-xIcocewAZARY~o91NR6jCuybYYWUZQ88czYzRB3DDsQXFto9myBpiWwjhxmUJkGSo0aCdoYee~VKebd6Va~9cdYz3Ylte~TFmLAYj8IVMS9KiRA5sLVJdI0EeEqF6JSXtZ~0dRpZCAe6Y3C1BYlFt7kSbAqa2udBDO49~ILCUEuPi83w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":62537,"name":"Porosity and Permeability in Reservoirs","url":"https://www.academia.edu/Documents/in/Porosity_and_Permeability_in_Reservoirs"},{"id":274524,"name":"Confinement","url":"https://www.academia.edu/Documents/in/Confinement"}],"urls":[{"id":19967113,"url":"http://www.nature.com/articles/s41598-019-56885-y.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772849"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772849/Simplification_of_complex_fracture_morphology_and_its_impact_on_production"><img alt="Research paper thumbnail of Simplification of complex fracture morphology and its impact on production" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772849/Simplification_of_complex_fracture_morphology_and_its_impact_on_production">Simplification of complex fracture morphology and its impact on production</a></div><div class="wp-workCard_item"><span>International Journal of Oil, Gas and Coal Technology</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Significant amounts of oil and natural gas in the USA are produced from fractured reservoirs. Fra...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Significant amounts of oil and natural gas in the USA are produced from fractured reservoirs. Fracture morphology and effectiveness of fracturing job depend on various factors such as geological properties (permeability, porosity, and heterogeneity), mechanical properties (Young&#39;s modulus, Poisson&#39;s ratio, stress anisotropy, maximum horizontal stress) and fracturing operational parameters (fluid injection rate, fluid viscosity). Reservoir engineer&#39;s job is to import the fracture geometry into reservoir flow simulator in order to forecast the production of hydrocarbons to evaluate a play&#39;s potential. In this research, various issues related to simplification of rigorously-generated fractures are investigated. A systematic approach including practical flow consideration in hydraulic fracture with heterogeneous permeability and width along the length is developed. The complex fractures morphology is simplified in two proposed models with mathematical formulations. Simplified models show promising alternatives in rapid forecasting of production of hydrocarbon without losing the characteristic of fracture properties like complex morphology and bottleneck. Oil recovery, cumulative gas oil ratio (GOR), oil rate and average reservoir pressure are compared with results from complex fracture morphology. One field case is used to demonstrate the validity of the method. [Received: May 9, 2017; Accepted: March 23, 2018]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772849"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772849"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772849; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772849]").text(description); $(".js-view-count[data-work-id=77772849]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772849; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772849']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772849, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772849]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772849,"title":"Simplification of complex fracture morphology and its impact on production","translated_title":"","metadata":{"abstract":"Significant amounts of oil and natural gas in the USA are produced from fractured reservoirs. Fracture morphology and effectiveness of fracturing job depend on various factors such as geological properties (permeability, porosity, and heterogeneity), mechanical properties (Young\u0026#39;s modulus, Poisson\u0026#39;s ratio, stress anisotropy, maximum horizontal stress) and fracturing operational parameters (fluid injection rate, fluid viscosity). Reservoir engineer\u0026#39;s job is to import the fracture geometry into reservoir flow simulator in order to forecast the production of hydrocarbons to evaluate a play\u0026#39;s potential. In this research, various issues related to simplification of rigorously-generated fractures are investigated. A systematic approach including practical flow consideration in hydraulic fracture with heterogeneous permeability and width along the length is developed. The complex fractures morphology is simplified in two proposed models with mathematical formulations. Simplified models show promising alternatives in rapid forecasting of production of hydrocarbon without losing the characteristic of fracture properties like complex morphology and bottleneck. Oil recovery, cumulative gas oil ratio (GOR), oil rate and average reservoir pressure are compared with results from complex fracture morphology. One field case is used to demonstrate the validity of the method. [Received: May 9, 2017; Accepted: March 23, 2018]","publisher":"Inderscience Publishers","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"International Journal of Oil, Gas and Coal Technology"},"translated_abstract":"Significant amounts of oil and natural gas in the USA are produced from fractured reservoirs. Fracture morphology and effectiveness of fracturing job depend on various factors such as geological properties (permeability, porosity, and heterogeneity), mechanical properties (Young\u0026#39;s modulus, Poisson\u0026#39;s ratio, stress anisotropy, maximum horizontal stress) and fracturing operational parameters (fluid injection rate, fluid viscosity). Reservoir engineer\u0026#39;s job is to import the fracture geometry into reservoir flow simulator in order to forecast the production of hydrocarbons to evaluate a play\u0026#39;s potential. In this research, various issues related to simplification of rigorously-generated fractures are investigated. A systematic approach including practical flow consideration in hydraulic fracture with heterogeneous permeability and width along the length is developed. The complex fractures morphology is simplified in two proposed models with mathematical formulations. Simplified models show promising alternatives in rapid forecasting of production of hydrocarbon without losing the characteristic of fracture properties like complex morphology and bottleneck. Oil recovery, cumulative gas oil ratio (GOR), oil rate and average reservoir pressure are compared with results from complex fracture morphology. One field case is used to demonstrate the validity of the method. [Received: May 9, 2017; Accepted: March 23, 2018]","internal_url":"https://www.academia.edu/77772849/Simplification_of_complex_fracture_morphology_and_its_impact_on_production","translated_internal_url":"","created_at":"2022-04-27T05:00:27.385-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Simplification_of_complex_fracture_morphology_and_its_impact_on_production","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"}],"urls":[{"id":19967112,"url":"http://www.inderscienceonline.com/doi/full/10.1504/IJOGCT.2020.105458"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772848"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772848/Stimulated_Oil_Reservoir_Volume_Estimation_of_Prominent_US_Tight_Oil_Formations"><img alt="Research paper thumbnail of Stimulated Oil Reservoir Volume Estimation of Prominent US Tight Oil Formations" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772848/Stimulated_Oil_Reservoir_Volume_Estimation_of_Prominent_US_Tight_Oil_Formations">Stimulated Oil Reservoir Volume Estimation of Prominent US Tight Oil Formations</a></div><div class="wp-workCard_item"><span>SPE Liquids-Rich Basins Conference - North America</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this work, we estimate the Stimulated Original Oil In Place (SOOIP) of hydraulically fractured...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this work, we estimate the Stimulated Original Oil In Place (SOOIP) of hydraulically fractured horizontal wells in prominent shale plays. This is done by compiling production data from hundreds of wells belonging to the Bakken, Niobrara, Wolfcamp, Eagle Ford, Bone Springs, and Woodford totaling over 2,500 wells. Additionally, we present probabilistic distributions of SOOIP with mean, standard deviation, P10, P50, and P90 estimates for each play. To circumvent the challenge of data availability for each well, we use the findings of a previous study where all reservoir unknowns are grouped into two major parameters. One of these parameters, alpha, is a function of the stimulated reservoir volume, compressibility, and pressure drawdown, where the last two are unknowns. While alpha is determined with high confidence for each well, we account for the uncertainty of compressibility and drawdown values across wells by assuming a normal distribution for these parameters. Lastly, by incor...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772848"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772848"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772848; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772848]").text(description); $(".js-view-count[data-work-id=77772848]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772848; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772848']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772848, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772848]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772848,"title":"Stimulated Oil Reservoir Volume Estimation of Prominent US Tight Oil Formations","translated_title":"","metadata":{"abstract":"In this work, we estimate the Stimulated Original Oil In Place (SOOIP) of hydraulically fractured horizontal wells in prominent shale plays. This is done by compiling production data from hundreds of wells belonging to the Bakken, Niobrara, Wolfcamp, Eagle Ford, Bone Springs, and Woodford totaling over 2,500 wells. Additionally, we present probabilistic distributions of SOOIP with mean, standard deviation, P10, P50, and P90 estimates for each play. To circumvent the challenge of data availability for each well, we use the findings of a previous study where all reservoir unknowns are grouped into two major parameters. One of these parameters, alpha, is a function of the stimulated reservoir volume, compressibility, and pressure drawdown, where the last two are unknowns. While alpha is determined with high confidence for each well, we account for the uncertainty of compressibility and drawdown values across wells by assuming a normal distribution for these parameters. Lastly, by incor...","publisher":"Society of Petroleum Engineers","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"SPE Liquids-Rich Basins Conference - North America"},"translated_abstract":"In this work, we estimate the Stimulated Original Oil In Place (SOOIP) of hydraulically fractured horizontal wells in prominent shale plays. This is done by compiling production data from hundreds of wells belonging to the Bakken, Niobrara, Wolfcamp, Eagle Ford, Bone Springs, and Woodford totaling over 2,500 wells. Additionally, we present probabilistic distributions of SOOIP with mean, standard deviation, P10, P50, and P90 estimates for each play. To circumvent the challenge of data availability for each well, we use the findings of a previous study where all reservoir unknowns are grouped into two major parameters. One of these parameters, alpha, is a function of the stimulated reservoir volume, compressibility, and pressure drawdown, where the last two are unknowns. While alpha is determined with high confidence for each well, we account for the uncertainty of compressibility and drawdown values across wells by assuming a normal distribution for these parameters. Lastly, by incor...","internal_url":"https://www.academia.edu/77772848/Stimulated_Oil_Reservoir_Volume_Estimation_of_Prominent_US_Tight_Oil_Formations","translated_internal_url":"","created_at":"2022-04-27T05:00:27.280-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Stimulated_Oil_Reservoir_Volume_Estimation_of_Prominent_US_Tight_Oil_Formations","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":561067,"name":"Decline Curve Analysis","url":"https://www.academia.edu/Documents/in/Decline_Curve_Analysis"},{"id":2590967,"name":"tight rock shale formations","url":"https://www.academia.edu/Documents/in/tight_rock_shale_formations"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772847"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772847/Fluid_flow_distribution_in_fractures_for_a_doublet_system_in_Enhanced_Geothermal_Systems_EGS_"><img alt="Research paper thumbnail of Fluid flow distribution in fractures for a doublet system in Enhanced Geothermal Systems (EGS)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772847/Fluid_flow_distribution_in_fractures_for_a_doublet_system_in_Enhanced_Geothermal_Systems_EGS_">Fluid flow distribution in fractures for a doublet system in Enhanced Geothermal Systems (EGS)</a></div><div class="wp-workCard_item"><span>Geothermics</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Extraction of heat from an enhanced geothermal system (EGS) is a renewable and environme...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Extraction of heat from an enhanced geothermal system (EGS) is a renewable and environmentally benign technology. Process involves circulation of colder water in hot rock through a flow path consisting of injection well, several vertical fractures, and production well. In this process, distribution of water among the vertical fractures is one of the key factors for optimization of heat recovery. Geometry such as dimensions or total flow area and fluid velocity in wells and fractures play major role in the hydrodynamics in the loop. A mathematical model is developed from the analogy of electrical circuit applying Kirchhoff’s law to determine the pressure drop between two points. Accordingly, the flow rates through fractures are calculated. Maintenance of sufficient pressure in a fracture is necessary to avoid closure due to horizontal stress. In this model, variation of fracture width with pressure is considered. The impacts of injection rate, well diameter and number of fractures on the distribution of flow in fractures are also investigated in this study. Since the frictional loss along the well decreases with the increase in well diameter, less variations of flow rates in fractures are observed. Similarly, low fluid velocity due to low total flow rate causes less frictional loss, thus more even distributions of flow in the fracture is observed. The number of fractures completed in an EGS is an important parameter for optimization. The flow distribution among the fractures depends on the total number of fractures present in the system. Although, more fractures improve the heat recovery, the cost of completion increases with the number of fracture. The analytical model for flow distribution developed in this study is helpful to evaluate the effectiveness of an EGS and to optimize the completion and operational parameters.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772847"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772847"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772847; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772847]").text(description); $(".js-view-count[data-work-id=77772847]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772847; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772847']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772847, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772847]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772847,"title":"Fluid flow distribution in fractures for a doublet system in Enhanced Geothermal Systems (EGS)","translated_title":"","metadata":{"abstract":"Abstract Extraction of heat from an enhanced geothermal system (EGS) is a renewable and environmentally benign technology. Process involves circulation of colder water in hot rock through a flow path consisting of injection well, several vertical fractures, and production well. In this process, distribution of water among the vertical fractures is one of the key factors for optimization of heat recovery. Geometry such as dimensions or total flow area and fluid velocity in wells and fractures play major role in the hydrodynamics in the loop. A mathematical model is developed from the analogy of electrical circuit applying Kirchhoff’s law to determine the pressure drop between two points. Accordingly, the flow rates through fractures are calculated. Maintenance of sufficient pressure in a fracture is necessary to avoid closure due to horizontal stress. In this model, variation of fracture width with pressure is considered. The impacts of injection rate, well diameter and number of fractures on the distribution of flow in fractures are also investigated in this study. Since the frictional loss along the well decreases with the increase in well diameter, less variations of flow rates in fractures are observed. Similarly, low fluid velocity due to low total flow rate causes less frictional loss, thus more even distributions of flow in the fracture is observed. The number of fractures completed in an EGS is an important parameter for optimization. The flow distribution among the fractures depends on the total number of fractures present in the system. Although, more fractures improve the heat recovery, the cost of completion increases with the number of fracture. The analytical model for flow distribution developed in this study is helpful to evaluate the effectiveness of an EGS and to optimize the completion and operational parameters.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Geothermics"},"translated_abstract":"Abstract Extraction of heat from an enhanced geothermal system (EGS) is a renewable and environmentally benign technology. Process involves circulation of colder water in hot rock through a flow path consisting of injection well, several vertical fractures, and production well. In this process, distribution of water among the vertical fractures is one of the key factors for optimization of heat recovery. Geometry such as dimensions or total flow area and fluid velocity in wells and fractures play major role in the hydrodynamics in the loop. A mathematical model is developed from the analogy of electrical circuit applying Kirchhoff’s law to determine the pressure drop between two points. Accordingly, the flow rates through fractures are calculated. Maintenance of sufficient pressure in a fracture is necessary to avoid closure due to horizontal stress. In this model, variation of fracture width with pressure is considered. The impacts of injection rate, well diameter and number of fractures on the distribution of flow in fractures are also investigated in this study. Since the frictional loss along the well decreases with the increase in well diameter, less variations of flow rates in fractures are observed. Similarly, low fluid velocity due to low total flow rate causes less frictional loss, thus more even distributions of flow in the fracture is observed. The number of fractures completed in an EGS is an important parameter for optimization. The flow distribution among the fractures depends on the total number of fractures present in the system. Although, more fractures improve the heat recovery, the cost of completion increases with the number of fracture. The analytical model for flow distribution developed in this study is helpful to evaluate the effectiveness of an EGS and to optimize the completion and operational parameters.","internal_url":"https://www.academia.edu/77772847/Fluid_flow_distribution_in_fractures_for_a_doublet_system_in_Enhanced_Geothermal_Systems_EGS_","translated_internal_url":"","created_at":"2022-04-27T05:00:27.123-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Fluid_flow_distribution_in_fractures_for_a_doublet_system_in_Enhanced_Geothermal_Systems_EGS_","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":409,"name":"Geophysics","url":"https://www.academia.edu/Documents/in/Geophysics"},{"id":223277,"name":"Geothermics","url":"https://www.academia.edu/Documents/in/Geothermics"}],"urls":[{"id":19967111,"url":"https://api.elsevier.com/content/article/PII:S0375650518300476?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772846"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772846/Experimental_Verification_of_Changing_Bubble_Points_of_Oils_in_Shales_Effect_of_Preferential_Absorption_by_Kerogen_and_Confinement_of_Fluids"><img alt="Research paper thumbnail of Experimental Verification of Changing Bubble Points of Oils in Shales: Effect of Preferential Absorption by Kerogen and Confinement of Fluids" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772846/Experimental_Verification_of_Changing_Bubble_Points_of_Oils_in_Shales_Effect_of_Preferential_Absorption_by_Kerogen_and_Confinement_of_Fluids">Experimental Verification of Changing Bubble Points of Oils in Shales: Effect of Preferential Absorption by Kerogen and Confinement of Fluids</a></div><div class="wp-workCard_item"><span>SPE Annual Technical Conference and Exhibition</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The economic and increased production of oil and gas from shale plays in the United States plays ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The economic and increased production of oil and gas from shale plays in the United States plays a key role in the country&#39;s energy independence. There are many factors that govern increased production of oil and gas from shales. One such factor is the assessment of the correct in-situ oil bubble point in shales which is critical in the optimization of hydrocarbon production. Shales are nano porous organic-rich sedimentary rocks that act as both source and reservoir oil and gas systems. The effect of nano pore confinement on the bubble point of oil in shales has been widely studied and documented in the SPE papers. However, the effect of organic matter presence on the bubble point of oil in shales has not been explored. The researchers at the University of Utah has studied both the effects by performing molecular scale simulations, thermodynamic modeling and experiments using analytical tools. This paper discusses the experimental effect of the presence of nano pores and organic...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772846"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772846"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772846; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772846]").text(description); $(".js-view-count[data-work-id=77772846]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772846; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772846']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772846, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772846]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772846,"title":"Experimental Verification of Changing Bubble Points of Oils in Shales: Effect of Preferential Absorption by Kerogen and Confinement of Fluids","translated_title":"","metadata":{"abstract":"The economic and increased production of oil and gas from shale plays in the United States plays a key role in the country\u0026#39;s energy independence. There are many factors that govern increased production of oil and gas from shales. One such factor is the assessment of the correct in-situ oil bubble point in shales which is critical in the optimization of hydrocarbon production. Shales are nano porous organic-rich sedimentary rocks that act as both source and reservoir oil and gas systems. The effect of nano pore confinement on the bubble point of oil in shales has been widely studied and documented in the SPE papers. However, the effect of organic matter presence on the bubble point of oil in shales has not been explored. The researchers at the University of Utah has studied both the effects by performing molecular scale simulations, thermodynamic modeling and experiments using analytical tools. This paper discusses the experimental effect of the presence of nano pores and organic...","publisher":"Society of Petroleum Engineers","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"SPE Annual Technical Conference and Exhibition"},"translated_abstract":"The economic and increased production of oil and gas from shale plays in the United States plays a key role in the country\u0026#39;s energy independence. There are many factors that govern increased production of oil and gas from shales. One such factor is the assessment of the correct in-situ oil bubble point in shales which is critical in the optimization of hydrocarbon production. Shales are nano porous organic-rich sedimentary rocks that act as both source and reservoir oil and gas systems. The effect of nano pore confinement on the bubble point of oil in shales has been widely studied and documented in the SPE papers. However, the effect of organic matter presence on the bubble point of oil in shales has not been explored. The researchers at the University of Utah has studied both the effects by performing molecular scale simulations, thermodynamic modeling and experiments using analytical tools. This paper discusses the experimental effect of the presence of nano pores and organic...","internal_url":"https://www.academia.edu/77772846/Experimental_Verification_of_Changing_Bubble_Points_of_Oils_in_Shales_Effect_of_Preferential_Absorption_by_Kerogen_and_Confinement_of_Fluids","translated_internal_url":"","created_at":"2022-04-27T05:00:26.998-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Experimental_Verification_of_Changing_Bubble_Points_of_Oils_in_Shales_Effect_of_Preferential_Absorption_by_Kerogen_and_Confinement_of_Fluids","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":33451,"name":"Oil Shale","url":"https://www.academia.edu/Documents/in/Oil_Shale"},{"id":867776,"name":"Kerogen","url":"https://www.academia.edu/Documents/in/Kerogen"},{"id":3568454,"name":"pore confinement","url":"https://www.academia.edu/Documents/in/pore_confinement"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772845"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772845/Simplification_workflow_for_hydraulically_fractured_reservoirs"><img alt="Research paper thumbnail of Simplification workflow for hydraulically fractured reservoirs" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772845/Simplification_workflow_for_hydraulically_fractured_reservoirs">Simplification workflow for hydraulically fractured reservoirs</a></div><div class="wp-workCard_item"><span>Petroleum</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Production from unconventional formations, such as shales, has significantly increased i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Production from unconventional formations, such as shales, has significantly increased in recent years by stimulating large portions of a reservoir through the application of horizontal drilling and hydraulic fracturing. Although oil shales are heavily dependent on oil prices, production forecasts remain positive in the North-American region. Due to the complexity of hydraulically fractured tight formations, reservoir numerical simulation has become the standard tool to assess and predict production performance from these unconventional resources. Many of these unconventional fields are immense, consisting of multistage and multiwell projects, which results in impractical simulation run times. Hence, simplification of large-scale simulation models is now common both in the industry and academia. Typical simplified models such as the “single fracture” approach do not often capture the physics of large-scale projects which results in inaccurate results. In this paper we present a simple, yet rigorous workflow that generates simplified representative models in order to achieve low simulation run times while capturing physical phenomena which is fundamental for accurate calculations. The proposed workflow is based on consideration of representative portions of a large-scale model followed by post-process scaling to obtain desired full model results. The simplified models that result from the application of the proposed workflow for a single well and a multiwell case are compared to full-scale models and the “single fracture” model. Comparison of fluid rates and cumulative production show that accurate results are possible for simplified models if all important components for a particular case are taken into account. Finally, application of the workflow is shown for a heterogeneous field case where prediction studies can be carried out.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772845"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772845"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772845; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772845]").text(description); $(".js-view-count[data-work-id=77772845]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772845; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772845']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772845, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772845]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772845,"title":"Simplification workflow for hydraulically fractured reservoirs","translated_title":"","metadata":{"abstract":"Abstract Production from unconventional formations, such as shales, has significantly increased in recent years by stimulating large portions of a reservoir through the application of horizontal drilling and hydraulic fracturing. Although oil shales are heavily dependent on oil prices, production forecasts remain positive in the North-American region. Due to the complexity of hydraulically fractured tight formations, reservoir numerical simulation has become the standard tool to assess and predict production performance from these unconventional resources. Many of these unconventional fields are immense, consisting of multistage and multiwell projects, which results in impractical simulation run times. Hence, simplification of large-scale simulation models is now common both in the industry and academia. Typical simplified models such as the “single fracture” approach do not often capture the physics of large-scale projects which results in inaccurate results. In this paper we present a simple, yet rigorous workflow that generates simplified representative models in order to achieve low simulation run times while capturing physical phenomena which is fundamental for accurate calculations. The proposed workflow is based on consideration of representative portions of a large-scale model followed by post-process scaling to obtain desired full model results. The simplified models that result from the application of the proposed workflow for a single well and a multiwell case are compared to full-scale models and the “single fracture” model. Comparison of fluid rates and cumulative production show that accurate results are possible for simplified models if all important components for a particular case are taken into account. Finally, application of the workflow is shown for a heterogeneous field case where prediction studies can be carried out.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Petroleum"},"translated_abstract":"Abstract Production from unconventional formations, such as shales, has significantly increased in recent years by stimulating large portions of a reservoir through the application of horizontal drilling and hydraulic fracturing. Although oil shales are heavily dependent on oil prices, production forecasts remain positive in the North-American region. Due to the complexity of hydraulically fractured tight formations, reservoir numerical simulation has become the standard tool to assess and predict production performance from these unconventional resources. Many of these unconventional fields are immense, consisting of multistage and multiwell projects, which results in impractical simulation run times. Hence, simplification of large-scale simulation models is now common both in the industry and academia. Typical simplified models such as the “single fracture” approach do not often capture the physics of large-scale projects which results in inaccurate results. In this paper we present a simple, yet rigorous workflow that generates simplified representative models in order to achieve low simulation run times while capturing physical phenomena which is fundamental for accurate calculations. The proposed workflow is based on consideration of representative portions of a large-scale model followed by post-process scaling to obtain desired full model results. The simplified models that result from the application of the proposed workflow for a single well and a multiwell case are compared to full-scale models and the “single fracture” model. Comparison of fluid rates and cumulative production show that accurate results are possible for simplified models if all important components for a particular case are taken into account. Finally, application of the workflow is shown for a heterogeneous field case where prediction studies can be carried out.","internal_url":"https://www.academia.edu/77772845/Simplification_workflow_for_hydraulically_fractured_reservoirs","translated_internal_url":"","created_at":"2022-04-27T05:00:26.863-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Simplification_workflow_for_hydraulically_fractured_reservoirs","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":5285,"name":"Petroleum","url":"https://www.academia.edu/Documents/in/Petroleum"},{"id":30896,"name":"Reservoir Simulation","url":"https://www.academia.edu/Documents/in/Reservoir_Simulation"},{"id":897823,"name":"Elsevier","url":"https://www.academia.edu/Documents/in/Elsevier"},{"id":2590967,"name":"tight rock shale formations","url":"https://www.academia.edu/Documents/in/tight_rock_shale_formations"}],"urls":[{"id":19967110,"url":"https://api.elsevier.com/content/article/PII:S240565611730072X?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772844"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772844/Analysis_of_North_American_Tight_oil_production"><img alt="Research paper thumbnail of Analysis of North-American Tight oil production" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772844/Analysis_of_North_American_Tight_oil_production">Analysis of North-American Tight oil production</a></div><div class="wp-workCard_item"><span>AIChE Journal</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">North-American tight oil production has been on the rise due to the introduction of new drilling ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">North-American tight oil production has been on the rise due to the introduction of new drilling and hydraulic fracturing technologies. Such advances have dramatically changed the conventional understanding of the hydrocarbon recovery process. A dimensionless study of tight oil production across the United Sates in plays such as the Bakken, Niobrara, Eagle Ford, Woodford, Bone Spring, and Wolfcamp shed light on some of these recovery processes. Production from any well, regardless of geologic attributes and operating conditions, fits into a universal curve during its initial productive period. Subsequently, production becomes a strong function of hydrocarbon thermodynamics and multiphase flow. Results from this analysis help rank important parameters that affect oil recovery in terms of how wells are operated and the reservoir&#39;s intrinsic geological and fluid properties. Furthermore, production results are combined with a simple dimensionless economic analysis to determine optimal fracture configurations independent of oil price environment. © 2017 American Institute of Chemical Engineers AIChE J, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772844"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772844"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772844; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772844]").text(description); $(".js-view-count[data-work-id=77772844]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772844; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772844']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772844, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772844]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772844,"title":"Analysis of North-American Tight oil production","translated_title":"","metadata":{"abstract":"North-American tight oil production has been on the rise due to the introduction of new drilling and hydraulic fracturing technologies. Such advances have dramatically changed the conventional understanding of the hydrocarbon recovery process. A dimensionless study of tight oil production across the United Sates in plays such as the Bakken, Niobrara, Eagle Ford, Woodford, Bone Spring, and Wolfcamp shed light on some of these recovery processes. Production from any well, regardless of geologic attributes and operating conditions, fits into a universal curve during its initial productive period. Subsequently, production becomes a strong function of hydrocarbon thermodynamics and multiphase flow. Results from this analysis help rank important parameters that affect oil recovery in terms of how wells are operated and the reservoir\u0026#39;s intrinsic geological and fluid properties. Furthermore, production results are combined with a simple dimensionless economic analysis to determine optimal fracture configurations independent of oil price environment. © 2017 American Institute of Chemical Engineers AIChE J, 2017","publisher":"Wiley","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"AIChE Journal"},"translated_abstract":"North-American tight oil production has been on the rise due to the introduction of new drilling and hydraulic fracturing technologies. Such advances have dramatically changed the conventional understanding of the hydrocarbon recovery process. A dimensionless study of tight oil production across the United Sates in plays such as the Bakken, Niobrara, Eagle Ford, Woodford, Bone Spring, and Wolfcamp shed light on some of these recovery processes. Production from any well, regardless of geologic attributes and operating conditions, fits into a universal curve during its initial productive period. Subsequently, production becomes a strong function of hydrocarbon thermodynamics and multiphase flow. Results from this analysis help rank important parameters that affect oil recovery in terms of how wells are operated and the reservoir\u0026#39;s intrinsic geological and fluid properties. Furthermore, production results are combined with a simple dimensionless economic analysis to determine optimal fracture configurations independent of oil price environment. © 2017 American Institute of Chemical Engineers AIChE J, 2017","internal_url":"https://www.academia.edu/77772844/Analysis_of_North_American_Tight_oil_production","translated_internal_url":"","created_at":"2022-04-27T05:00:26.739-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Analysis_of_North_American_Tight_oil_production","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":72,"name":"Chemical Engineering","url":"https://www.academia.edu/Documents/in/Chemical_Engineering"},{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":357894,"name":"Oil Production","url":"https://www.academia.edu/Documents/in/Oil_Production"},{"id":2820942,"name":"Aiche","url":"https://www.academia.edu/Documents/in/Aiche"}],"urls":[{"id":19967109,"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Faic.16034"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772843"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772843/Effect_of_Different_Flow_Schemes_on_Heat_Recovery_from_Enhanced_Geothermal_Systems_EGS_"><img alt="Research paper thumbnail of Effect of Different Flow Schemes on Heat Recovery from Enhanced Geothermal Systems (EGS)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772843/Effect_of_Different_Flow_Schemes_on_Heat_Recovery_from_Enhanced_Geothermal_Systems_EGS_">Effect of Different Flow Schemes on Heat Recovery from Enhanced Geothermal Systems (EGS)</a></div><div class="wp-workCard_item"><span>Energy</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Operational optimization is the key to maximize the heat extraction efficiency of Enhanced Geothe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Operational optimization is the key to maximize the heat extraction efficiency of Enhanced Geothermal Systems (EGS). Injection/production flowrate is one of the operational parameters that can be easily manipulated to produce desired amount of energy. In this study, the effect of different flow schemes on the rate of heat production is analyzed over a period of 30 years. Seven flow schemes (four continuous functions namely constant flow, linear flow, exponential flow, mirror exponential flow, and three step functions with step sizes of six months, three years and ten years) developed on the basis of mathematical functions were examined. A doublet EGS model with a single fracture was simulated using a commercial thermal reservoir simulator. The reservoir and well data were obtained from the FORGE (Frontier Observatory for Research in Geothermal Energy) site at Milford Utah. The results were analyzed on the basis of their temperature decline curves for the produced water and the total amount of heat extracted over the entire period. The exponential flow scheme is the optimum case considering the rise in energy demand over the next 30 years. The amount of heat extracted per unit volume of water decreases with increase in total water volume circulated.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772843"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772843"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772843; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772843]").text(description); $(".js-view-count[data-work-id=77772843]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772843; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772843']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772843, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772843]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772843,"title":"Effect of Different Flow Schemes on Heat Recovery from Enhanced Geothermal Systems (EGS)","translated_title":"","metadata":{"abstract":"Operational optimization is the key to maximize the heat extraction efficiency of Enhanced Geothermal Systems (EGS). Injection/production flowrate is one of the operational parameters that can be easily manipulated to produce desired amount of energy. In this study, the effect of different flow schemes on the rate of heat production is analyzed over a period of 30 years. Seven flow schemes (four continuous functions namely constant flow, linear flow, exponential flow, mirror exponential flow, and three step functions with step sizes of six months, three years and ten years) developed on the basis of mathematical functions were examined. A doublet EGS model with a single fracture was simulated using a commercial thermal reservoir simulator. The reservoir and well data were obtained from the FORGE (Frontier Observatory for Research in Geothermal Energy) site at Milford Utah. The results were analyzed on the basis of their temperature decline curves for the produced water and the total amount of heat extracted over the entire period. The exponential flow scheme is the optimum case considering the rise in energy demand over the next 30 years. The amount of heat extracted per unit volume of water decreases with increase in total water volume circulated.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Energy"},"translated_abstract":"Operational optimization is the key to maximize the heat extraction efficiency of Enhanced Geothermal Systems (EGS). Injection/production flowrate is one of the operational parameters that can be easily manipulated to produce desired amount of energy. In this study, the effect of different flow schemes on the rate of heat production is analyzed over a period of 30 years. Seven flow schemes (four continuous functions namely constant flow, linear flow, exponential flow, mirror exponential flow, and three step functions with step sizes of six months, three years and ten years) developed on the basis of mathematical functions were examined. A doublet EGS model with a single fracture was simulated using a commercial thermal reservoir simulator. The reservoir and well data were obtained from the FORGE (Frontier Observatory for Research in Geothermal Energy) site at Milford Utah. The results were analyzed on the basis of their temperature decline curves for the produced water and the total amount of heat extracted over the entire period. The exponential flow scheme is the optimum case considering the rise in energy demand over the next 30 years. The amount of heat extracted per unit volume of water decreases with increase in total water volume circulated.","internal_url":"https://www.academia.edu/77772843/Effect_of_Different_Flow_Schemes_on_Heat_Recovery_from_Enhanced_Geothermal_Systems_EGS_","translated_internal_url":"","created_at":"2022-04-27T05:00:26.601-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Effect_of_Different_Flow_Schemes_on_Heat_Recovery_from_Enhanced_Geothermal_Systems_EGS_","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering"},{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":5412,"name":"Energy","url":"https://www.academia.edu/Documents/in/Energy"},{"id":51531,"name":"Pergamon","url":"https://www.academia.edu/Documents/in/Pergamon"},{"id":554780,"name":"Interdisciplinary Engineering","url":"https://www.academia.edu/Documents/in/Interdisciplinary_Engineering"},{"id":2600527,"name":"Enhanced Geothermal System (EGS)","url":"https://www.academia.edu/Documents/in/Enhanced_Geothermal_System_EGS_"}],"urls":[{"id":19967108,"url":"https://api.elsevier.com/content/article/PII:S0360544219305407?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772842"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772842/Performance_evaluation_of_enhanced_geothermal_system_EGS_Surrogate_models_sensitivity_study_and_ranking_key_parameters"><img alt="Research paper thumbnail of Performance evaluation of enhanced geothermal system (EGS): Surrogate models, sensitivity study and ranking key parameters" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772842/Performance_evaluation_of_enhanced_geothermal_system_EGS_Surrogate_models_sensitivity_study_and_ranking_key_parameters">Performance evaluation of enhanced geothermal system (EGS): Surrogate models, sensitivity study and ranking key parameters</a></div><div class="wp-workCard_item"><span>Renewable Energy</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Designing an efficient system to extract heat from an enhanced geothermal system (EGS) r...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Designing an efficient system to extract heat from an enhanced geothermal system (EGS) requires proper understanding of the behavior of the reservoir over a long period. Five key parameters namely well spacing, fracture spacing, well inclination angle, injection temperature and injection rate are considered in this study for a doublet well system. To study and evaluate the performance of an EGS, second order surrogate models for ‘produced water temperature’, at certain time intervals are developed as a function of these five factors. The in-situ properties of a candidate reservoir for designing the simulations are taken from the FORGE site, Utah. Simulations are designed using ‘Box-Behnken’ design of experiments techniques to minimize the number of simulations. The models are trained and tested with the simulated results. Fitness of the models is calculated by estimating the errors using the coefficient of determination (R2) and the normalized root mean square error (NRMSE). These surrogate models are used to study the sensitivity of the aforementioned factors on the temperature of the produced water and the heat recovery over a time period of 30 years. Finally, the hierarchy of factors, as they impact the total heat recovery are represented as a tornado plot.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772842"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772842"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772842; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772842]").text(description); $(".js-view-count[data-work-id=77772842]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772842; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772842']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772842, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772842]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772842,"title":"Performance evaluation of enhanced geothermal system (EGS): Surrogate models, sensitivity study and ranking key parameters","translated_title":"","metadata":{"abstract":"Abstract Designing an efficient system to extract heat from an enhanced geothermal system (EGS) requires proper understanding of the behavior of the reservoir over a long period. Five key parameters namely well spacing, fracture spacing, well inclination angle, injection temperature and injection rate are considered in this study for a doublet well system. To study and evaluate the performance of an EGS, second order surrogate models for ‘produced water temperature’, at certain time intervals are developed as a function of these five factors. The in-situ properties of a candidate reservoir for designing the simulations are taken from the FORGE site, Utah. Simulations are designed using ‘Box-Behnken’ design of experiments techniques to minimize the number of simulations. The models are trained and tested with the simulated results. Fitness of the models is calculated by estimating the errors using the coefficient of determination (R2) and the normalized root mean square error (NRMSE). These surrogate models are used to study the sensitivity of the aforementioned factors on the temperature of the produced water and the heat recovery over a time period of 30 years. Finally, the hierarchy of factors, as they impact the total heat recovery are represented as a tornado plot.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Renewable Energy"},"translated_abstract":"Abstract Designing an efficient system to extract heat from an enhanced geothermal system (EGS) requires proper understanding of the behavior of the reservoir over a long period. Five key parameters namely well spacing, fracture spacing, well inclination angle, injection temperature and injection rate are considered in this study for a doublet well system. To study and evaluate the performance of an EGS, second order surrogate models for ‘produced water temperature’, at certain time intervals are developed as a function of these five factors. The in-situ properties of a candidate reservoir for designing the simulations are taken from the FORGE site, Utah. Simulations are designed using ‘Box-Behnken’ design of experiments techniques to minimize the number of simulations. The models are trained and tested with the simulated results. Fitness of the models is calculated by estimating the errors using the coefficient of determination (R2) and the normalized root mean square error (NRMSE). These surrogate models are used to study the sensitivity of the aforementioned factors on the temperature of the produced water and the heat recovery over a time period of 30 years. Finally, the hierarchy of factors, as they impact the total heat recovery are represented as a tornado plot.","internal_url":"https://www.academia.edu/77772842/Performance_evaluation_of_enhanced_geothermal_system_EGS_Surrogate_models_sensitivity_study_and_ranking_key_parameters","translated_internal_url":"","created_at":"2022-04-27T05:00:26.460-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Performance_evaluation_of_enhanced_geothermal_system_EGS_Surrogate_models_sensitivity_study_and_ranking_key_parameters","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering"},{"id":2738,"name":"Renewable Energy","url":"https://www.academia.edu/Documents/in/Renewable_Energy"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering"},{"id":2600527,"name":"Enhanced Geothermal System (EGS)","url":"https://www.academia.edu/Documents/in/Enhanced_Geothermal_System_EGS_"}],"urls":[{"id":19967107,"url":"https://api.elsevier.com/content/article/PII:S0960148118301083?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772841"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772841/Efficient_workflow_for_simulation_of_multifractured_enhanced_geothermal_systems_EGS_"><img alt="Research paper thumbnail of Efficient workflow for simulation of multifractured enhanced geothermal systems (EGS)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772841/Efficient_workflow_for_simulation_of_multifractured_enhanced_geothermal_systems_EGS_">Efficient workflow for simulation of multifractured enhanced geothermal systems (EGS)</a></div><div class="wp-workCard_item"><span>Renewable Energy</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The increasing demand for clean energy with minimum environmental impact motivates development of...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The increasing demand for clean energy with minimum environmental impact motivates development of geothermal energy. Simulating a geothermal reservoir is complex and time consuming, mainly because of the systems spatial and temporal non-isothermal nature and the enormous size of the domain/reservoir. Simulations become even more complex when representing Enhanced Geothermal Systems (EGS), where wells in a hot, low permeability reservoir are interconnected by hydraulic fracturing to provide pathways for injection of cold water, in situ heating, and consequent production of hot water. In this study, various issues related to simulation of enhanced geothermal systems are investigated and practical solutions are proposed. A comprehensive study was conducted to show the effect of different grid systems on predictions of the transient temperature of the produced water. It is shown that the performance of an EGS is affected by the transmissivity (product of permeability and width of the fracture) of the fracture more so than by the values of permeability and width of the fracture considered individually. A simplified model (downscaled model) reduces the simulation times significantly (by 1.5–14.5 times) without compromising the accuracy of the results. In the proposed model, only two simulations - capturing small portions of the top and bottom of a reservoir with two active hydraulic fractures is used to evaluate performance of the entire reservoir. The proposed model is proved to be robust when exposed to different scenarios created by varying the inclination of the wells with respect to horizontal, spacing of the hydraulic factures, and spacing between the injection and producing wells. Value of R2 close to unity (0.96–1.0) and smaller value of MAPE (Mean Absolute Percentage Error), less than 3% in comparison to the entire reservoir simulations, indicate the utility of proposed model.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772841"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772841"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772841; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772841]").text(description); $(".js-view-count[data-work-id=77772841]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772841; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772841']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772841, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772841]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772841,"title":"Efficient workflow for simulation of multifractured enhanced geothermal systems (EGS)","translated_title":"","metadata":{"abstract":"The increasing demand for clean energy with minimum environmental impact motivates development of geothermal energy. Simulating a geothermal reservoir is complex and time consuming, mainly because of the systems spatial and temporal non-isothermal nature and the enormous size of the domain/reservoir. Simulations become even more complex when representing Enhanced Geothermal Systems (EGS), where wells in a hot, low permeability reservoir are interconnected by hydraulic fracturing to provide pathways for injection of cold water, in situ heating, and consequent production of hot water. In this study, various issues related to simulation of enhanced geothermal systems are investigated and practical solutions are proposed. A comprehensive study was conducted to show the effect of different grid systems on predictions of the transient temperature of the produced water. It is shown that the performance of an EGS is affected by the transmissivity (product of permeability and width of the fracture) of the fracture more so than by the values of permeability and width of the fracture considered individually. A simplified model (downscaled model) reduces the simulation times significantly (by 1.5–14.5 times) without compromising the accuracy of the results. In the proposed model, only two simulations - capturing small portions of the top and bottom of a reservoir with two active hydraulic fractures is used to evaluate performance of the entire reservoir. The proposed model is proved to be robust when exposed to different scenarios created by varying the inclination of the wells with respect to horizontal, spacing of the hydraulic factures, and spacing between the injection and producing wells. Value of R2 close to unity (0.96–1.0) and smaller value of MAPE (Mean Absolute Percentage Error), less than 3% in comparison to the entire reservoir simulations, indicate the utility of proposed model.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Renewable Energy"},"translated_abstract":"The increasing demand for clean energy with minimum environmental impact motivates development of geothermal energy. Simulating a geothermal reservoir is complex and time consuming, mainly because of the systems spatial and temporal non-isothermal nature and the enormous size of the domain/reservoir. Simulations become even more complex when representing Enhanced Geothermal Systems (EGS), where wells in a hot, low permeability reservoir are interconnected by hydraulic fracturing to provide pathways for injection of cold water, in situ heating, and consequent production of hot water. In this study, various issues related to simulation of enhanced geothermal systems are investigated and practical solutions are proposed. A comprehensive study was conducted to show the effect of different grid systems on predictions of the transient temperature of the produced water. It is shown that the performance of an EGS is affected by the transmissivity (product of permeability and width of the fracture) of the fracture more so than by the values of permeability and width of the fracture considered individually. A simplified model (downscaled model) reduces the simulation times significantly (by 1.5–14.5 times) without compromising the accuracy of the results. In the proposed model, only two simulations - capturing small portions of the top and bottom of a reservoir with two active hydraulic fractures is used to evaluate performance of the entire reservoir. The proposed model is proved to be robust when exposed to different scenarios created by varying the inclination of the wells with respect to horizontal, spacing of the hydraulic factures, and spacing between the injection and producing wells. Value of R2 close to unity (0.96–1.0) and smaller value of MAPE (Mean Absolute Percentage Error), less than 3% in comparison to the entire reservoir simulations, indicate the utility of proposed model.","internal_url":"https://www.academia.edu/77772841/Efficient_workflow_for_simulation_of_multifractured_enhanced_geothermal_systems_EGS_","translated_internal_url":"","created_at":"2022-04-27T05:00:26.258-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Efficient_workflow_for_simulation_of_multifractured_enhanced_geothermal_systems_EGS_","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering"},{"id":2738,"name":"Renewable Energy","url":"https://www.academia.edu/Documents/in/Renewable_Energy"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering"},{"id":2600527,"name":"Enhanced Geothermal System (EGS)","url":"https://www.academia.edu/Documents/in/Enhanced_Geothermal_System_EGS_"}],"urls":[{"id":19967106,"url":"https://api.elsevier.com/content/article/PII:S0960148118308711?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772839"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772839/Green_extraction_methods_of_food_polyphenols_from_vegetable_materials"><img alt="Research paper thumbnail of Green extraction methods of food polyphenols from vegetable materials" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772839/Green_extraction_methods_of_food_polyphenols_from_vegetable_materials">Green extraction methods of food polyphenols from vegetable materials</a></div><div class="wp-workCard_item"><span>Current Opinion in Food Science</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Green extraction methods are being developed using modern technology where less or no organic sol...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Green extraction methods are being developed using modern technology where less or no organic solvents are used to minimize environmental and health impacts and to maximize the yield of desired polyphenols by selective extraction. Advanced methods such as microwave assisted, ultrasound assisted, pulsed electric field assisted and enzyme assisted extractions, as well as pressurized liquid and supercritical fluid extractions are given more emphasis. The theory behind some advanced extractions methods is described. A brief review of applications of extractions from various parts of plants such as roots, fruits, seeds, leaves, vegetables, barks, etc. are provided.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772839"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772839"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772839; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772839]").text(description); $(".js-view-count[data-work-id=77772839]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772839; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772839']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772839, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772839]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772839,"title":"Green extraction methods of food polyphenols from vegetable materials","translated_title":"","metadata":{"abstract":"Green extraction methods are being developed using modern technology where less or no organic solvents are used to minimize environmental and health impacts and to maximize the yield of desired polyphenols by selective extraction. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="6479648" id="papers"><div class="js-work-strip profile--work_container" data-work-id="93349087"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/93349087/Injection_of_Flue_Gas_Improves_CO2_Permeability_and_Storage_Capacity_in_Coal_A_Promising_Technology"><img alt="Research paper thumbnail of Injection of Flue Gas Improves CO2 Permeability and Storage Capacity in Coal: A Promising Technology" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/93349087/Injection_of_Flue_Gas_Improves_CO2_Permeability_and_Storage_Capacity_in_Coal_A_Promising_Technology">Injection of Flue Gas Improves CO2 Permeability and Storage Capacity in Coal: A Promising Technology</a></div><div class="wp-workCard_item"><span>Day 2 Tue, October 04, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Swelling of coal thus reducing permeability is the main detrimental for any carbon dioxide (CO2) ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Swelling of coal thus reducing permeability is the main detrimental for any carbon dioxide (CO2) capture and storage (CCS) projects. Additionally, CO2 capture from flue gas or direct air is an expensive process. The current commercial simulators are impaired of combining various effects such as fluid segregation, adsorption, Darcy&#39;s flow, and permeability change in coal. The objective of this study is to develop a numerical model to simulate flue gas injection in coal. The study is motivated by encouraging preliminary results from lab-scale experiments of injection of flue gas (ideally a mixture of Nitrogen and CO2) in coal. Bench-scale experiments demonstrated the swelling reduction caused by the selective flow of surrogate flue gas N2-CO2 mixture, based on the fluid stratification at sub-critical conditions where the density of pure components in a vertical container causes stratification as predicted from the Grashof number and the thermodynamic properties of fluids. The nume...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="93349087"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="93349087"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 93349087; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=93349087]").text(description); $(".js-view-count[data-work-id=93349087]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 93349087; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='93349087']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 93349087, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=93349087]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":93349087,"title":"Injection of Flue Gas Improves CO2 Permeability and Storage Capacity in Coal: A Promising Technology","translated_title":"","metadata":{"abstract":"Swelling of coal thus reducing permeability is the main detrimental for any carbon dioxide (CO2) capture and storage (CCS) projects. Additionally, CO2 capture from flue gas or direct air is an expensive process. The current commercial simulators are impaired of combining various effects such as fluid segregation, adsorption, Darcy\u0026#39;s flow, and permeability change in coal. The objective of this study is to develop a numerical model to simulate flue gas injection in coal. The study is motivated by encouraging preliminary results from lab-scale experiments of injection of flue gas (ideally a mixture of Nitrogen and CO2) in coal. Bench-scale experiments demonstrated the swelling reduction caused by the selective flow of surrogate flue gas N2-CO2 mixture, based on the fluid stratification at sub-critical conditions where the density of pure components in a vertical container causes stratification as predicted from the Grashof number and the thermodynamic properties of fluids. The nume...","publisher":"SPE","publication_name":"Day 2 Tue, October 04, 2022"},"translated_abstract":"Swelling of coal thus reducing permeability is the main detrimental for any carbon dioxide (CO2) capture and storage (CCS) projects. Additionally, CO2 capture from flue gas or direct air is an expensive process. The current commercial simulators are impaired of combining various effects such as fluid segregation, adsorption, Darcy\u0026#39;s flow, and permeability change in coal. The objective of this study is to develop a numerical model to simulate flue gas injection in coal. The study is motivated by encouraging preliminary results from lab-scale experiments of injection of flue gas (ideally a mixture of Nitrogen and CO2) in coal. Bench-scale experiments demonstrated the swelling reduction caused by the selective flow of surrogate flue gas N2-CO2 mixture, based on the fluid stratification at sub-critical conditions where the density of pure components in a vertical container causes stratification as predicted from the Grashof number and the thermodynamic properties of fluids. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772855"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772855/Enhanced_Recovery_in_Shales_Molecular_Investigation_of_CO2_Energized_Fluid_for_Re_Fracturing_Shale_Formations"><img alt="Research paper thumbnail of Enhanced Recovery in Shales: Molecular Investigation of CO2 Energized Fluid for Re-Fracturing Shale Formations" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772855/Enhanced_Recovery_in_Shales_Molecular_Investigation_of_CO2_Energized_Fluid_for_Re_Fracturing_Shale_Formations">Enhanced Recovery in Shales: Molecular Investigation of CO2 Energized Fluid for Re-Fracturing Shale Formations</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The liquid and gas rich shales are low permeability, low porosity but high organic content reserv...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The liquid and gas rich shales are low permeability, low porosity but high organic content reservoirs. They need effective sub-surface, in-situ stimulation for economic production. Sometimes due to ineffective initial completion, the shale wells don&amp;#39;t produce well. An effective re-stimulation or refracturing can shoot up the production from these mature low producing wells. The fracture fluid plays a key role in determining the fate of such reservoirs. Typically, slickwater or gelled water is used as a fracture-fluid. However, the energized fluids, energized with carbon dioxide (CO 2) has shown to increase the performance. They reduce the water volumes, problems associated with water usage and have superior proppant transport capabilities. The Molecular Dynamics (MD) Simulations technique is used in the current work to understand the interaction between carbon dioxide and hydrocarbons rich Type II kerogen in the shale rocks. The Nose-Hoover style non-Hamiltonian equations of motion are used in a molecular simulator to generate positions and velocities of carbon dioxide and kerogen molecules sampled from the canonical (nvt) and isothermal-isobaric (npt) ensembles. In this work, we propose that carbon dioxide energized fluids be used for re-fracturing the low producing shale formations. In shale fractured with carbon dioxide enhanced fracture fluid, some of the CO 2 is retained in the formation and doesn&amp;#39;t flow back after the fracturing operation. The current work study the fate of the retained portion of CO 2 in an energized fracturing operation. MD simulations reveal that carbon dioxide has more affinity than methane and heavier hydrocarbons like octane, to be retained in the organic part known as kerogen (Pathak M., 2015a) found in shales. MD simulations also reveal that the kerogen shrinks as a results of absorption of CO2 which leads to effective decrease in the skin of the formation. This helps in better fluid flow between the formation and fractures. The carbon dioxide dissolved in the kerogen helps to displace hydrocarbons absorbed in the kerogen. The diffusion coefficient of carbon dioxide in kerogen is found to be of an order of magnitude less than methane or octane. MD simulations technique has been used for the first time to explore the interaction between carbon dioxide and organic matter in shales at the molecular scale. The mechanism of the carbon dioxide energized fracture-fluid induced enhanced recovery is understood to understand the key processes that helps in taking decisions of a re-fracture job.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772855"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772855"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772855; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772855]").text(description); $(".js-view-count[data-work-id=77772855]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772855; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772855']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772855, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772855]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772855,"title":"Enhanced Recovery in Shales: Molecular Investigation of CO2 Energized Fluid for Re-Fracturing Shale Formations","translated_title":"","metadata":{"abstract":"The liquid and gas rich shales are low permeability, low porosity but high organic content reservoirs. They need effective sub-surface, in-situ stimulation for economic production. Sometimes due to ineffective initial completion, the shale wells don\u0026amp;#39;t produce well. An effective re-stimulation or refracturing can shoot up the production from these mature low producing wells. The fracture fluid plays a key role in determining the fate of such reservoirs. Typically, slickwater or gelled water is used as a fracture-fluid. However, the energized fluids, energized with carbon dioxide (CO 2) has shown to increase the performance. They reduce the water volumes, problems associated with water usage and have superior proppant transport capabilities. The Molecular Dynamics (MD) Simulations technique is used in the current work to understand the interaction between carbon dioxide and hydrocarbons rich Type II kerogen in the shale rocks. The Nose-Hoover style non-Hamiltonian equations of motion are used in a molecular simulator to generate positions and velocities of carbon dioxide and kerogen molecules sampled from the canonical (nvt) and isothermal-isobaric (npt) ensembles. In this work, we propose that carbon dioxide energized fluids be used for re-fracturing the low producing shale formations. In shale fractured with carbon dioxide enhanced fracture fluid, some of the CO 2 is retained in the formation and doesn\u0026amp;#39;t flow back after the fracturing operation. The current work study the fate of the retained portion of CO 2 in an energized fracturing operation. MD simulations reveal that carbon dioxide has more affinity than methane and heavier hydrocarbons like octane, to be retained in the organic part known as kerogen (Pathak M., 2015a) found in shales. MD simulations also reveal that the kerogen shrinks as a results of absorption of CO2 which leads to effective decrease in the skin of the formation. This helps in better fluid flow between the formation and fractures. The carbon dioxide dissolved in the kerogen helps to displace hydrocarbons absorbed in the kerogen. The diffusion coefficient of carbon dioxide in kerogen is found to be of an order of magnitude less than methane or octane. MD simulations technique has been used for the first time to explore the interaction between carbon dioxide and organic matter in shales at the molecular scale. The mechanism of the carbon dioxide energized fracture-fluid induced enhanced recovery is understood to understand the key processes that helps in taking decisions of a re-fracture job.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}}},"translated_abstract":"The liquid and gas rich shales are low permeability, low porosity but high organic content reservoirs. They need effective sub-surface, in-situ stimulation for economic production. Sometimes due to ineffective initial completion, the shale wells don\u0026amp;#39;t produce well. An effective re-stimulation or refracturing can shoot up the production from these mature low producing wells. The fracture fluid plays a key role in determining the fate of such reservoirs. Typically, slickwater or gelled water is used as a fracture-fluid. However, the energized fluids, energized with carbon dioxide (CO 2) has shown to increase the performance. They reduce the water volumes, problems associated with water usage and have superior proppant transport capabilities. The Molecular Dynamics (MD) Simulations technique is used in the current work to understand the interaction between carbon dioxide and hydrocarbons rich Type II kerogen in the shale rocks. The Nose-Hoover style non-Hamiltonian equations of motion are used in a molecular simulator to generate positions and velocities of carbon dioxide and kerogen molecules sampled from the canonical (nvt) and isothermal-isobaric (npt) ensembles. In this work, we propose that carbon dioxide energized fluids be used for re-fracturing the low producing shale formations. In shale fractured with carbon dioxide enhanced fracture fluid, some of the CO 2 is retained in the formation and doesn\u0026amp;#39;t flow back after the fracturing operation. The current work study the fate of the retained portion of CO 2 in an energized fracturing operation. MD simulations reveal that carbon dioxide has more affinity than methane and heavier hydrocarbons like octane, to be retained in the organic part known as kerogen (Pathak M., 2015a) found in shales. MD simulations also reveal that the kerogen shrinks as a results of absorption of CO2 which leads to effective decrease in the skin of the formation. This helps in better fluid flow between the formation and fractures. The carbon dioxide dissolved in the kerogen helps to displace hydrocarbons absorbed in the kerogen. The diffusion coefficient of carbon dioxide in kerogen is found to be of an order of magnitude less than methane or octane. MD simulations technique has been used for the first time to explore the interaction between carbon dioxide and organic matter in shales at the molecular scale. The mechanism of the carbon dioxide energized fracture-fluid induced enhanced recovery is understood to understand the key processes that helps in taking decisions of a re-fracture job.","internal_url":"https://www.academia.edu/77772855/Enhanced_Recovery_in_Shales_Molecular_Investigation_of_CO2_Energized_Fluid_for_Re_Fracturing_Shale_Formations","translated_internal_url":"","created_at":"2022-04-27T05:00:27.878-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Enhanced_Recovery_in_Shales_Molecular_Investigation_of_CO2_Energized_Fluid_for_Re_Fracturing_Shale_Formations","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":29056,"name":"Hydraulic Fracturing","url":"https://www.academia.edu/Documents/in/Hydraulic_Fracturing"},{"id":33451,"name":"Oil Shale","url":"https://www.academia.edu/Documents/in/Oil_Shale"},{"id":333293,"name":"Shale gas","url":"https://www.academia.edu/Documents/in/Shale_gas"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772853"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772853/Production_of_Liquid_Hydrocarbons_from_Shales"><img alt="Research paper thumbnail of Production of Liquid Hydrocarbons from Shales" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772853/Production_of_Liquid_Hydrocarbons_from_Shales">Production of Liquid Hydrocarbons from Shales</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Shale and mudstone are sedimentary rocks composed of clay-sized particles. These clayey rocks are...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Shale and mudstone are sedimentary rocks composed of clay-sized particles. These clayey rocks are excellent candidates for petroleum source rocks because they are relatively rich in organic matter (kerogen) dispersed in the rock pore space. Kerogen is thermally cracked to oil and gas at depths under temperatures of 60–150 C over geologic time scales (millions of years). Some of the generated oil and gas migrates to porous reservoirs enclosed by traps where they create conventional prospects. However, a considerable amount of hydrocarbon remains within the source rock, thus making shale and mudstone a source rock as well as a reservoir. Liquid hydrocarbons refer to light and heavy crude oils and condensates. Liquid condensate is obtained from wet gas and gas-condensate reservoirs. Appropriate drilling and stimulation technologies are essential to achieve economic rates of production from shales due to the ultralow permeabilities and very low porosities found in these rocks. Tight shale reservoirs are complex and pose many challenges to petroleum production. Some of these challenges revolve around well placement, hydraulic fracture spacing, drilling operations and scheduling optimization, environmental impact minimization, etc. while following strict regulations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772853"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772853"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772853; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772853]").text(description); $(".js-view-count[data-work-id=77772853]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772853; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772853']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772853, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772853]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772853,"title":"Production of Liquid Hydrocarbons from Shales","translated_title":"","metadata":{"abstract":"Shale and mudstone are sedimentary rocks composed of clay-sized particles. These clayey rocks are excellent candidates for petroleum source rocks because they are relatively rich in organic matter (kerogen) dispersed in the rock pore space. Kerogen is thermally cracked to oil and gas at depths under temperatures of 60–150 \u0001C over geologic time scales (millions of years). Some of the generated oil and gas migrates to porous reservoirs enclosed by traps where they create conventional prospects. However, a considerable amount of hydrocarbon remains within the source rock, thus making shale and mudstone a source rock as well as a reservoir. Liquid hydrocarbons refer to light and heavy crude oils and condensates. Liquid condensate is obtained from wet gas and gas-condensate reservoirs. Appropriate drilling and stimulation technologies are essential to achieve economic rates of production from shales due to the ultralow permeabilities and very low porosities found in these rocks. Tight shale reservoirs are complex and pose many challenges to petroleum production. Some of these challenges revolve around well placement, hydraulic fracture spacing, drilling operations and scheduling optimization, environmental impact minimization, etc. while following strict regulations.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}}},"translated_abstract":"Shale and mudstone are sedimentary rocks composed of clay-sized particles. These clayey rocks are excellent candidates for petroleum source rocks because they are relatively rich in organic matter (kerogen) dispersed in the rock pore space. Kerogen is thermally cracked to oil and gas at depths under temperatures of 60–150 \u0001C over geologic time scales (millions of years). Some of the generated oil and gas migrates to porous reservoirs enclosed by traps where they create conventional prospects. However, a considerable amount of hydrocarbon remains within the source rock, thus making shale and mudstone a source rock as well as a reservoir. Liquid hydrocarbons refer to light and heavy crude oils and condensates. Liquid condensate is obtained from wet gas and gas-condensate reservoirs. Appropriate drilling and stimulation technologies are essential to achieve economic rates of production from shales due to the ultralow permeabilities and very low porosities found in these rocks. Tight shale reservoirs are complex and pose many challenges to petroleum production. Some of these challenges revolve around well placement, hydraulic fracture spacing, drilling operations and scheduling optimization, environmental impact minimization, etc. while following strict regulations.","internal_url":"https://www.academia.edu/77772853/Production_of_Liquid_Hydrocarbons_from_Shales","translated_internal_url":"","created_at":"2022-04-27T05:00:27.782-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Production_of_Liquid_Hydrocarbons_from_Shales","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":33451,"name":"Oil Shale","url":"https://www.academia.edu/Documents/in/Oil_Shale"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772851"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772851/What_Happens_to_Permeability_at_the_Nanoscale_A_Molecular_Dynamics_Simulation_Study"><img alt="Research paper thumbnail of What Happens to Permeability at the Nanoscale? A Molecular Dynamics Simulation Study" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772851/What_Happens_to_Permeability_at_the_Nanoscale_A_Molecular_Dynamics_Simulation_Study">What Happens to Permeability at the Nanoscale? A Molecular Dynamics Simulation Study</a></div><div class="wp-workCard_item"><span>Proceedings of the 5th Unconventional Resources Technology Conference</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">As one of the industry&amp;#39;s corner stones, the concept of permeability is used anywhere from...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">As one of the industry&amp;#39;s corner stones, the concept of permeability is used anywhere from reservoir characterization to full-fledged reservoir simulation models. Permeability is defined as a rock property that describes the ease of fluid flow, implying that permeability is fundamentally independent of fluid. This concept has worked successfully for conventional reservoirs and is now well accepted in the industry. However, as pore dimensions approach the nanoscale in unconventional reservoirs, we must re-evaluate the validity of a fluid-independent permeability concept. Does our conventional understanding of permeability break as we reach nanoscale pores? If so, how do rock-fluid interactions affect fluid flow and permeability calculations? We approximate the porous medium as a collection of long cylinders with and without wall roughness. The permeability from this pseudo-porous system is determined as a function of pore throat diameter using Darcy&amp;#39;s law and the Hagen-Poiseuille equation. The result is a permeability versus pore throat diameter relationship based on continuum mechanics with a no-slip boundary condition at the rock walls. To investigate the effects of rock-fluid interactions on permeability, we use molecular dynamics to simulate the pseudo-porous geometry with carbon nanotubes flowing water, hexane, and a mixture. Initially, we observe that continuum permeability and molecular simulation permeability converge for all fluids; however, as pore throat diameter shrinks into the nanoscale, continuum and molecular permeabilities deviate significantly. The extent at which deviations occur depends on the type of fluid, pore throat diameter, and whether the carbon nanotube is hydrophobic or hydrophilic. In addition, the effects of rock-fluid interactions also affect multiphase behavior resulting in different relative permeability curves depending on the pore throat diameter. Recently, research has been aimed towards the study of phase behavior and transport properties of numerous fluids inside carbon nanotubes. In this work, we study and quantify the effects of rock-fluid interactions on permeability and its consequences on liquid multiphase flow when pore throats reach nanoscale dimensions. The implications of a fluid-dependent permeability concept at the nanoscale have enormous interdisciplinary ramifications and could lead to a better understanding of unconventional reservoirs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772851"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772851"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772851; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772851]").text(description); $(".js-view-count[data-work-id=77772851]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772851; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772851']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772851, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772851]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772851,"title":"What Happens to Permeability at the Nanoscale? A Molecular Dynamics Simulation Study","translated_title":"","metadata":{"abstract":"As one of the industry\u0026amp;#39;s corner stones, the concept of permeability is used anywhere from reservoir characterization to full-fledged reservoir simulation models. Permeability is defined as a rock property that describes the ease of fluid flow, implying that permeability is fundamentally independent of fluid. This concept has worked successfully for conventional reservoirs and is now well accepted in the industry. However, as pore dimensions approach the nanoscale in unconventional reservoirs, we must re-evaluate the validity of a fluid-independent permeability concept. Does our conventional understanding of permeability break as we reach nanoscale pores? If so, how do rock-fluid interactions affect fluid flow and permeability calculations? We approximate the porous medium as a collection of long cylinders with and without wall roughness. The permeability from this pseudo-porous system is determined as a function of pore throat diameter using Darcy\u0026amp;#39;s law and the Hagen-Poiseuille equation. The result is a permeability versus pore throat diameter relationship based on continuum mechanics with a no-slip boundary condition at the rock walls. To investigate the effects of rock-fluid interactions on permeability, we use molecular dynamics to simulate the pseudo-porous geometry with carbon nanotubes flowing water, hexane, and a mixture. Initially, we observe that continuum permeability and molecular simulation permeability converge for all fluids; however, as pore throat diameter shrinks into the nanoscale, continuum and molecular permeabilities deviate significantly. The extent at which deviations occur depends on the type of fluid, pore throat diameter, and whether the carbon nanotube is hydrophobic or hydrophilic. In addition, the effects of rock-fluid interactions also affect multiphase behavior resulting in different relative permeability curves depending on the pore throat diameter. Recently, research has been aimed towards the study of phase behavior and transport properties of numerous fluids inside carbon nanotubes. In this work, we study and quantify the effects of rock-fluid interactions on permeability and its consequences on liquid multiphase flow when pore throats reach nanoscale dimensions. The implications of a fluid-dependent permeability concept at the nanoscale have enormous interdisciplinary ramifications and could lead to a better understanding of unconventional reservoirs.","publisher":"American Association of Petroleum Geologists","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Proceedings of the 5th Unconventional Resources Technology Conference"},"translated_abstract":"As one of the industry\u0026amp;#39;s corner stones, the concept of permeability is used anywhere from reservoir characterization to full-fledged reservoir simulation models. Permeability is defined as a rock property that describes the ease of fluid flow, implying that permeability is fundamentally independent of fluid. This concept has worked successfully for conventional reservoirs and is now well accepted in the industry. However, as pore dimensions approach the nanoscale in unconventional reservoirs, we must re-evaluate the validity of a fluid-independent permeability concept. Does our conventional understanding of permeability break as we reach nanoscale pores? If so, how do rock-fluid interactions affect fluid flow and permeability calculations? We approximate the porous medium as a collection of long cylinders with and without wall roughness. The permeability from this pseudo-porous system is determined as a function of pore throat diameter using Darcy\u0026amp;#39;s law and the Hagen-Poiseuille equation. The result is a permeability versus pore throat diameter relationship based on continuum mechanics with a no-slip boundary condition at the rock walls. To investigate the effects of rock-fluid interactions on permeability, we use molecular dynamics to simulate the pseudo-porous geometry with carbon nanotubes flowing water, hexane, and a mixture. Initially, we observe that continuum permeability and molecular simulation permeability converge for all fluids; however, as pore throat diameter shrinks into the nanoscale, continuum and molecular permeabilities deviate significantly. The extent at which deviations occur depends on the type of fluid, pore throat diameter, and whether the carbon nanotube is hydrophobic or hydrophilic. In addition, the effects of rock-fluid interactions also affect multiphase behavior resulting in different relative permeability curves depending on the pore throat diameter. Recently, research has been aimed towards the study of phase behavior and transport properties of numerous fluids inside carbon nanotubes. In this work, we study and quantify the effects of rock-fluid interactions on permeability and its consequences on liquid multiphase flow when pore throats reach nanoscale dimensions. The implications of a fluid-dependent permeability concept at the nanoscale have enormous interdisciplinary ramifications and could lead to a better understanding of unconventional reservoirs.","internal_url":"https://www.academia.edu/77772851/What_Happens_to_Permeability_at_the_Nanoscale_A_Molecular_Dynamics_Simulation_Study","translated_internal_url":"","created_at":"2022-04-27T05:00:27.684-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"What_Happens_to_Permeability_at_the_Nanoscale_A_Molecular_Dynamics_Simulation_Study","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":2736,"name":"Molecular Dynamics Simulation","url":"https://www.academia.edu/Documents/in/Molecular_Dynamics_Simulation"},{"id":62537,"name":"Porosity and Permeability in Reservoirs","url":"https://www.academia.edu/Documents/in/Porosity_and_Permeability_in_Reservoirs"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772850"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772850/Confinement_Effect_on_Porosity_and_Permeability_of_Shales"><img alt="Research paper thumbnail of Confinement Effect on Porosity and Permeability of Shales" class="work-thumbnail" src="https://attachments.academia-assets.com/85049200/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772850/Confinement_Effect_on_Porosity_and_Permeability_of_Shales">Confinement Effect on Porosity and Permeability of Shales</a></div><div class="wp-workCard_item"><span>Scientific Reports</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Porosity and permeability are the key factors in assessing the hydrocarbon productivity of unconv...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Porosity and permeability are the key factors in assessing the hydrocarbon productivity of unconventional (shale) reservoirs, which are complex in nature due to their heterogeneous mineralogy and poorly connected nano- and micro-pore systems. Experimental efforts to measure these petrophysical properties posse many limitations, because they often take weeks to complete and are difficult to reproduce. Alternatively, numerical simulations can be conducted in digital rock 3D models reconstructed from image datasets acquired via e.g., nanoscale-resolution focused ion beam–scanning electron microscopy (FIB-SEM) nano-tomography. In this study, impact of reservoir confinement (stress) on porosity and permeability of shales was investigated using two digital rock 3D models, which represented nanoporous organic/mineral microstructure of the Marcellus Shale. Five stress scenarios were simulated for different depths (2,000–6,000 feet) within the production interval of a typical oil/gas reservo...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="69cd23b2e21032b23a410a40f4fb7858" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85049200,"asset_id":77772850,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85049200/download_file?st=MTczMjc5NzE1MCw4LjIyMi4yMDguMTQ2&st=MTczMjc5NzE0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772850"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772850"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772850; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772850]").text(description); $(".js-view-count[data-work-id=77772850]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772850; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772850']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772850, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "69cd23b2e21032b23a410a40f4fb7858" } } $('.js-work-strip[data-work-id=77772850]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772850,"title":"Confinement Effect on Porosity and Permeability of Shales","translated_title":"","metadata":{"abstract":"Porosity and permeability are the key factors in assessing the hydrocarbon productivity of unconventional (shale) reservoirs, which are complex in nature due to their heterogeneous mineralogy and poorly connected nano- and micro-pore systems. Experimental efforts to measure these petrophysical properties posse many limitations, because they often take weeks to complete and are difficult to reproduce. Alternatively, numerical simulations can be conducted in digital rock 3D models reconstructed from image datasets acquired via e.g., nanoscale-resolution focused ion beam–scanning electron microscopy (FIB-SEM) nano-tomography. In this study, impact of reservoir confinement (stress) on porosity and permeability of shales was investigated using two digital rock 3D models, which represented nanoporous organic/mineral microstructure of the Marcellus Shale. Five stress scenarios were simulated for different depths (2,000–6,000 feet) within the production interval of a typical oil/gas reservo...","publisher":"Springer Science and Business Media LLC","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Scientific Reports"},"translated_abstract":"Porosity and permeability are the key factors in assessing the hydrocarbon productivity of unconventional (shale) reservoirs, which are complex in nature due to their heterogeneous mineralogy and poorly connected nano- and micro-pore systems. Experimental efforts to measure these petrophysical properties posse many limitations, because they often take weeks to complete and are difficult to reproduce. Alternatively, numerical simulations can be conducted in digital rock 3D models reconstructed from image datasets acquired via e.g., nanoscale-resolution focused ion beam–scanning electron microscopy (FIB-SEM) nano-tomography. In this study, impact of reservoir confinement (stress) on porosity and permeability of shales was investigated using two digital rock 3D models, which represented nanoporous organic/mineral microstructure of the Marcellus Shale. Five stress scenarios were simulated for different depths (2,000–6,000 feet) within the production interval of a typical oil/gas reservo...","internal_url":"https://www.academia.edu/77772850/Confinement_Effect_on_Porosity_and_Permeability_of_Shales","translated_internal_url":"","created_at":"2022-04-27T05:00:27.539-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":85049200,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85049200/thumbnails/1.jpg","file_name":"s41598-019-56885-y.pdf","download_url":"https://www.academia.edu/attachments/85049200/download_file?st=MTczMjc5NzE1MCw4LjIyMi4yMDguMTQ2&st=MTczMjc5NzE0OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Confinement_Effect_on_Porosity_and_Perme.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85049200/s41598-019-56885-y-libre.pdf?1651061713=\u0026response-content-disposition=attachment%3B+filename%3DConfinement_Effect_on_Porosity_and_Perme.pdf\u0026Expires=1732800749\u0026Signature=V3ebMdueUofOy8BSPU4xaN8XPTY31IbJiNpbfHSWUc4p9eq2gqFd93DkMx33yX0gsGgvCsM4g-meKsf~CEeGa-ZMpJgTjZzkVvemqA0137~ici9OO7fhMH14ZrymwYg8c0JTB9xbY4QYEd7qSvZlE1DXaorQa3yt-B2ztvDgOkSFuCi~xrzoV4jNID7PYAYCd~TrEzNJ4~sLl1vrjPITkW-XmhRtsuP6fRmdx2UYrf57DtrWOPcsUz5KBASoQiWfJxbq4BqtLU4~jBn~9dg2hOnUwH9VoeT~j3ljbpMBu8gKt5XqiyWI3VggmGVvL9s9KtnTidUIxK2aaRfQL0EHpQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Confinement_Effect_on_Porosity_and_Permeability_of_Shales","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[{"id":85049200,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85049200/thumbnails/1.jpg","file_name":"s41598-019-56885-y.pdf","download_url":"https://www.academia.edu/attachments/85049200/download_file?st=MTczMjc5NzE1MCw4LjIyMi4yMDguMTQ2&st=MTczMjc5NzE0OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Confinement_Effect_on_Porosity_and_Perme.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85049200/s41598-019-56885-y-libre.pdf?1651061713=\u0026response-content-disposition=attachment%3B+filename%3DConfinement_Effect_on_Porosity_and_Perme.pdf\u0026Expires=1732800749\u0026Signature=V3ebMdueUofOy8BSPU4xaN8XPTY31IbJiNpbfHSWUc4p9eq2gqFd93DkMx33yX0gsGgvCsM4g-meKsf~CEeGa-ZMpJgTjZzkVvemqA0137~ici9OO7fhMH14ZrymwYg8c0JTB9xbY4QYEd7qSvZlE1DXaorQa3yt-B2ztvDgOkSFuCi~xrzoV4jNID7PYAYCd~TrEzNJ4~sLl1vrjPITkW-XmhRtsuP6fRmdx2UYrf57DtrWOPcsUz5KBASoQiWfJxbq4BqtLU4~jBn~9dg2hOnUwH9VoeT~j3ljbpMBu8gKt5XqiyWI3VggmGVvL9s9KtnTidUIxK2aaRfQL0EHpQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":85049201,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85049201/thumbnails/1.jpg","file_name":"s41598-019-56885-y.pdf","download_url":"https://www.academia.edu/attachments/85049201/download_file","bulk_download_file_name":"Confinement_Effect_on_Porosity_and_Perme.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85049201/s41598-019-56885-y-libre.pdf?1651061712=\u0026response-content-disposition=attachment%3B+filename%3DConfinement_Effect_on_Porosity_and_Perme.pdf\u0026Expires=1732800749\u0026Signature=A0P~0e~KNpCsgol4fVdAqJDVKBT-1Ooa3x8ojrcrkdyuzav6pcWOmuLQhCVYaJ6r8tCInZ6QDfQyPKx-Yf-e-C9wlaEUIk5s2ImWaq2bZw-SEeLkZMAiIJXQMA-UioIeH7V02Ri1aV1qQgpRURcK6o9czVT2RpjG~u1zb-xIcocewAZARY~o91NR6jCuybYYWUZQ88czYzRB3DDsQXFto9myBpiWwjhxmUJkGSo0aCdoYee~VKebd6Va~9cdYz3Ylte~TFmLAYj8IVMS9KiRA5sLVJdI0EeEqF6JSXtZ~0dRpZCAe6Y3C1BYlFt7kSbAqa2udBDO49~ILCUEuPi83w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":62537,"name":"Porosity and Permeability in Reservoirs","url":"https://www.academia.edu/Documents/in/Porosity_and_Permeability_in_Reservoirs"},{"id":274524,"name":"Confinement","url":"https://www.academia.edu/Documents/in/Confinement"}],"urls":[{"id":19967113,"url":"http://www.nature.com/articles/s41598-019-56885-y.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772849"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772849/Simplification_of_complex_fracture_morphology_and_its_impact_on_production"><img alt="Research paper thumbnail of Simplification of complex fracture morphology and its impact on production" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772849/Simplification_of_complex_fracture_morphology_and_its_impact_on_production">Simplification of complex fracture morphology and its impact on production</a></div><div class="wp-workCard_item"><span>International Journal of Oil, Gas and Coal Technology</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Significant amounts of oil and natural gas in the USA are produced from fractured reservoirs. Fra...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Significant amounts of oil and natural gas in the USA are produced from fractured reservoirs. Fracture morphology and effectiveness of fracturing job depend on various factors such as geological properties (permeability, porosity, and heterogeneity), mechanical properties (Young&#39;s modulus, Poisson&#39;s ratio, stress anisotropy, maximum horizontal stress) and fracturing operational parameters (fluid injection rate, fluid viscosity). Reservoir engineer&#39;s job is to import the fracture geometry into reservoir flow simulator in order to forecast the production of hydrocarbons to evaluate a play&#39;s potential. In this research, various issues related to simplification of rigorously-generated fractures are investigated. A systematic approach including practical flow consideration in hydraulic fracture with heterogeneous permeability and width along the length is developed. The complex fractures morphology is simplified in two proposed models with mathematical formulations. Simplified models show promising alternatives in rapid forecasting of production of hydrocarbon without losing the characteristic of fracture properties like complex morphology and bottleneck. Oil recovery, cumulative gas oil ratio (GOR), oil rate and average reservoir pressure are compared with results from complex fracture morphology. One field case is used to demonstrate the validity of the method. [Received: May 9, 2017; Accepted: March 23, 2018]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772849"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772849"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772849; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772849]").text(description); $(".js-view-count[data-work-id=77772849]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772849; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772849']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772849, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772849]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772849,"title":"Simplification of complex fracture morphology and its impact on production","translated_title":"","metadata":{"abstract":"Significant amounts of oil and natural gas in the USA are produced from fractured reservoirs. Fracture morphology and effectiveness of fracturing job depend on various factors such as geological properties (permeability, porosity, and heterogeneity), mechanical properties (Young\u0026#39;s modulus, Poisson\u0026#39;s ratio, stress anisotropy, maximum horizontal stress) and fracturing operational parameters (fluid injection rate, fluid viscosity). Reservoir engineer\u0026#39;s job is to import the fracture geometry into reservoir flow simulator in order to forecast the production of hydrocarbons to evaluate a play\u0026#39;s potential. In this research, various issues related to simplification of rigorously-generated fractures are investigated. A systematic approach including practical flow consideration in hydraulic fracture with heterogeneous permeability and width along the length is developed. The complex fractures morphology is simplified in two proposed models with mathematical formulations. Simplified models show promising alternatives in rapid forecasting of production of hydrocarbon without losing the characteristic of fracture properties like complex morphology and bottleneck. Oil recovery, cumulative gas oil ratio (GOR), oil rate and average reservoir pressure are compared with results from complex fracture morphology. One field case is used to demonstrate the validity of the method. [Received: May 9, 2017; Accepted: March 23, 2018]","publisher":"Inderscience Publishers","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"International Journal of Oil, Gas and Coal Technology"},"translated_abstract":"Significant amounts of oil and natural gas in the USA are produced from fractured reservoirs. Fracture morphology and effectiveness of fracturing job depend on various factors such as geological properties (permeability, porosity, and heterogeneity), mechanical properties (Young\u0026#39;s modulus, Poisson\u0026#39;s ratio, stress anisotropy, maximum horizontal stress) and fracturing operational parameters (fluid injection rate, fluid viscosity). Reservoir engineer\u0026#39;s job is to import the fracture geometry into reservoir flow simulator in order to forecast the production of hydrocarbons to evaluate a play\u0026#39;s potential. In this research, various issues related to simplification of rigorously-generated fractures are investigated. A systematic approach including practical flow consideration in hydraulic fracture with heterogeneous permeability and width along the length is developed. The complex fractures morphology is simplified in two proposed models with mathematical formulations. Simplified models show promising alternatives in rapid forecasting of production of hydrocarbon without losing the characteristic of fracture properties like complex morphology and bottleneck. Oil recovery, cumulative gas oil ratio (GOR), oil rate and average reservoir pressure are compared with results from complex fracture morphology. One field case is used to demonstrate the validity of the method. [Received: May 9, 2017; Accepted: March 23, 2018]","internal_url":"https://www.academia.edu/77772849/Simplification_of_complex_fracture_morphology_and_its_impact_on_production","translated_internal_url":"","created_at":"2022-04-27T05:00:27.385-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Simplification_of_complex_fracture_morphology_and_its_impact_on_production","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"}],"urls":[{"id":19967112,"url":"http://www.inderscienceonline.com/doi/full/10.1504/IJOGCT.2020.105458"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772848"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772848/Stimulated_Oil_Reservoir_Volume_Estimation_of_Prominent_US_Tight_Oil_Formations"><img alt="Research paper thumbnail of Stimulated Oil Reservoir Volume Estimation of Prominent US Tight Oil Formations" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772848/Stimulated_Oil_Reservoir_Volume_Estimation_of_Prominent_US_Tight_Oil_Formations">Stimulated Oil Reservoir Volume Estimation of Prominent US Tight Oil Formations</a></div><div class="wp-workCard_item"><span>SPE Liquids-Rich Basins Conference - North America</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this work, we estimate the Stimulated Original Oil In Place (SOOIP) of hydraulically fractured...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this work, we estimate the Stimulated Original Oil In Place (SOOIP) of hydraulically fractured horizontal wells in prominent shale plays. This is done by compiling production data from hundreds of wells belonging to the Bakken, Niobrara, Wolfcamp, Eagle Ford, Bone Springs, and Woodford totaling over 2,500 wells. Additionally, we present probabilistic distributions of SOOIP with mean, standard deviation, P10, P50, and P90 estimates for each play. To circumvent the challenge of data availability for each well, we use the findings of a previous study where all reservoir unknowns are grouped into two major parameters. One of these parameters, alpha, is a function of the stimulated reservoir volume, compressibility, and pressure drawdown, where the last two are unknowns. While alpha is determined with high confidence for each well, we account for the uncertainty of compressibility and drawdown values across wells by assuming a normal distribution for these parameters. Lastly, by incor...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772848"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772848"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772848; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772848]").text(description); $(".js-view-count[data-work-id=77772848]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772848; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772848']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772848, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772848]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772848,"title":"Stimulated Oil Reservoir Volume Estimation of Prominent US Tight Oil Formations","translated_title":"","metadata":{"abstract":"In this work, we estimate the Stimulated Original Oil In Place (SOOIP) of hydraulically fractured horizontal wells in prominent shale plays. This is done by compiling production data from hundreds of wells belonging to the Bakken, Niobrara, Wolfcamp, Eagle Ford, Bone Springs, and Woodford totaling over 2,500 wells. Additionally, we present probabilistic distributions of SOOIP with mean, standard deviation, P10, P50, and P90 estimates for each play. To circumvent the challenge of data availability for each well, we use the findings of a previous study where all reservoir unknowns are grouped into two major parameters. One of these parameters, alpha, is a function of the stimulated reservoir volume, compressibility, and pressure drawdown, where the last two are unknowns. While alpha is determined with high confidence for each well, we account for the uncertainty of compressibility and drawdown values across wells by assuming a normal distribution for these parameters. Lastly, by incor...","publisher":"Society of Petroleum Engineers","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"SPE Liquids-Rich Basins Conference - North America"},"translated_abstract":"In this work, we estimate the Stimulated Original Oil In Place (SOOIP) of hydraulically fractured horizontal wells in prominent shale plays. This is done by compiling production data from hundreds of wells belonging to the Bakken, Niobrara, Wolfcamp, Eagle Ford, Bone Springs, and Woodford totaling over 2,500 wells. Additionally, we present probabilistic distributions of SOOIP with mean, standard deviation, P10, P50, and P90 estimates for each play. To circumvent the challenge of data availability for each well, we use the findings of a previous study where all reservoir unknowns are grouped into two major parameters. One of these parameters, alpha, is a function of the stimulated reservoir volume, compressibility, and pressure drawdown, where the last two are unknowns. While alpha is determined with high confidence for each well, we account for the uncertainty of compressibility and drawdown values across wells by assuming a normal distribution for these parameters. Lastly, by incor...","internal_url":"https://www.academia.edu/77772848/Stimulated_Oil_Reservoir_Volume_Estimation_of_Prominent_US_Tight_Oil_Formations","translated_internal_url":"","created_at":"2022-04-27T05:00:27.280-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Stimulated_Oil_Reservoir_Volume_Estimation_of_Prominent_US_Tight_Oil_Formations","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":561067,"name":"Decline Curve Analysis","url":"https://www.academia.edu/Documents/in/Decline_Curve_Analysis"},{"id":2590967,"name":"tight rock shale formations","url":"https://www.academia.edu/Documents/in/tight_rock_shale_formations"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772847"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772847/Fluid_flow_distribution_in_fractures_for_a_doublet_system_in_Enhanced_Geothermal_Systems_EGS_"><img alt="Research paper thumbnail of Fluid flow distribution in fractures for a doublet system in Enhanced Geothermal Systems (EGS)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772847/Fluid_flow_distribution_in_fractures_for_a_doublet_system_in_Enhanced_Geothermal_Systems_EGS_">Fluid flow distribution in fractures for a doublet system in Enhanced Geothermal Systems (EGS)</a></div><div class="wp-workCard_item"><span>Geothermics</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Extraction of heat from an enhanced geothermal system (EGS) is a renewable and environme...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Extraction of heat from an enhanced geothermal system (EGS) is a renewable and environmentally benign technology. Process involves circulation of colder water in hot rock through a flow path consisting of injection well, several vertical fractures, and production well. In this process, distribution of water among the vertical fractures is one of the key factors for optimization of heat recovery. Geometry such as dimensions or total flow area and fluid velocity in wells and fractures play major role in the hydrodynamics in the loop. A mathematical model is developed from the analogy of electrical circuit applying Kirchhoff’s law to determine the pressure drop between two points. Accordingly, the flow rates through fractures are calculated. Maintenance of sufficient pressure in a fracture is necessary to avoid closure due to horizontal stress. In this model, variation of fracture width with pressure is considered. The impacts of injection rate, well diameter and number of fractures on the distribution of flow in fractures are also investigated in this study. Since the frictional loss along the well decreases with the increase in well diameter, less variations of flow rates in fractures are observed. Similarly, low fluid velocity due to low total flow rate causes less frictional loss, thus more even distributions of flow in the fracture is observed. The number of fractures completed in an EGS is an important parameter for optimization. The flow distribution among the fractures depends on the total number of fractures present in the system. Although, more fractures improve the heat recovery, the cost of completion increases with the number of fracture. The analytical model for flow distribution developed in this study is helpful to evaluate the effectiveness of an EGS and to optimize the completion and operational parameters.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772847"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772847"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772847; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772847]").text(description); $(".js-view-count[data-work-id=77772847]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772847; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772847']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772847, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772847]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772847,"title":"Fluid flow distribution in fractures for a doublet system in Enhanced Geothermal Systems (EGS)","translated_title":"","metadata":{"abstract":"Abstract Extraction of heat from an enhanced geothermal system (EGS) is a renewable and environmentally benign technology. Process involves circulation of colder water in hot rock through a flow path consisting of injection well, several vertical fractures, and production well. In this process, distribution of water among the vertical fractures is one of the key factors for optimization of heat recovery. Geometry such as dimensions or total flow area and fluid velocity in wells and fractures play major role in the hydrodynamics in the loop. A mathematical model is developed from the analogy of electrical circuit applying Kirchhoff’s law to determine the pressure drop between two points. Accordingly, the flow rates through fractures are calculated. Maintenance of sufficient pressure in a fracture is necessary to avoid closure due to horizontal stress. In this model, variation of fracture width with pressure is considered. The impacts of injection rate, well diameter and number of fractures on the distribution of flow in fractures are also investigated in this study. Since the frictional loss along the well decreases with the increase in well diameter, less variations of flow rates in fractures are observed. Similarly, low fluid velocity due to low total flow rate causes less frictional loss, thus more even distributions of flow in the fracture is observed. The number of fractures completed in an EGS is an important parameter for optimization. The flow distribution among the fractures depends on the total number of fractures present in the system. Although, more fractures improve the heat recovery, the cost of completion increases with the number of fracture. The analytical model for flow distribution developed in this study is helpful to evaluate the effectiveness of an EGS and to optimize the completion and operational parameters.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Geothermics"},"translated_abstract":"Abstract Extraction of heat from an enhanced geothermal system (EGS) is a renewable and environmentally benign technology. Process involves circulation of colder water in hot rock through a flow path consisting of injection well, several vertical fractures, and production well. In this process, distribution of water among the vertical fractures is one of the key factors for optimization of heat recovery. Geometry such as dimensions or total flow area and fluid velocity in wells and fractures play major role in the hydrodynamics in the loop. A mathematical model is developed from the analogy of electrical circuit applying Kirchhoff’s law to determine the pressure drop between two points. Accordingly, the flow rates through fractures are calculated. Maintenance of sufficient pressure in a fracture is necessary to avoid closure due to horizontal stress. In this model, variation of fracture width with pressure is considered. The impacts of injection rate, well diameter and number of fractures on the distribution of flow in fractures are also investigated in this study. Since the frictional loss along the well decreases with the increase in well diameter, less variations of flow rates in fractures are observed. Similarly, low fluid velocity due to low total flow rate causes less frictional loss, thus more even distributions of flow in the fracture is observed. The number of fractures completed in an EGS is an important parameter for optimization. The flow distribution among the fractures depends on the total number of fractures present in the system. Although, more fractures improve the heat recovery, the cost of completion increases with the number of fracture. The analytical model for flow distribution developed in this study is helpful to evaluate the effectiveness of an EGS and to optimize the completion and operational parameters.","internal_url":"https://www.academia.edu/77772847/Fluid_flow_distribution_in_fractures_for_a_doublet_system_in_Enhanced_Geothermal_Systems_EGS_","translated_internal_url":"","created_at":"2022-04-27T05:00:27.123-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Fluid_flow_distribution_in_fractures_for_a_doublet_system_in_Enhanced_Geothermal_Systems_EGS_","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":409,"name":"Geophysics","url":"https://www.academia.edu/Documents/in/Geophysics"},{"id":223277,"name":"Geothermics","url":"https://www.academia.edu/Documents/in/Geothermics"}],"urls":[{"id":19967111,"url":"https://api.elsevier.com/content/article/PII:S0375650518300476?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772846"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772846/Experimental_Verification_of_Changing_Bubble_Points_of_Oils_in_Shales_Effect_of_Preferential_Absorption_by_Kerogen_and_Confinement_of_Fluids"><img alt="Research paper thumbnail of Experimental Verification of Changing Bubble Points of Oils in Shales: Effect of Preferential Absorption by Kerogen and Confinement of Fluids" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772846/Experimental_Verification_of_Changing_Bubble_Points_of_Oils_in_Shales_Effect_of_Preferential_Absorption_by_Kerogen_and_Confinement_of_Fluids">Experimental Verification of Changing Bubble Points of Oils in Shales: Effect of Preferential Absorption by Kerogen and Confinement of Fluids</a></div><div class="wp-workCard_item"><span>SPE Annual Technical Conference and Exhibition</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The economic and increased production of oil and gas from shale plays in the United States plays ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The economic and increased production of oil and gas from shale plays in the United States plays a key role in the country&#39;s energy independence. There are many factors that govern increased production of oil and gas from shales. One such factor is the assessment of the correct in-situ oil bubble point in shales which is critical in the optimization of hydrocarbon production. Shales are nano porous organic-rich sedimentary rocks that act as both source and reservoir oil and gas systems. The effect of nano pore confinement on the bubble point of oil in shales has been widely studied and documented in the SPE papers. However, the effect of organic matter presence on the bubble point of oil in shales has not been explored. The researchers at the University of Utah has studied both the effects by performing molecular scale simulations, thermodynamic modeling and experiments using analytical tools. This paper discusses the experimental effect of the presence of nano pores and organic...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772846"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772846"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772846; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772846]").text(description); $(".js-view-count[data-work-id=77772846]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772846; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772846']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772846, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772846]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772846,"title":"Experimental Verification of Changing Bubble Points of Oils in Shales: Effect of Preferential Absorption by Kerogen and Confinement of Fluids","translated_title":"","metadata":{"abstract":"The economic and increased production of oil and gas from shale plays in the United States plays a key role in the country\u0026#39;s energy independence. There are many factors that govern increased production of oil and gas from shales. One such factor is the assessment of the correct in-situ oil bubble point in shales which is critical in the optimization of hydrocarbon production. Shales are nano porous organic-rich sedimentary rocks that act as both source and reservoir oil and gas systems. The effect of nano pore confinement on the bubble point of oil in shales has been widely studied and documented in the SPE papers. However, the effect of organic matter presence on the bubble point of oil in shales has not been explored. The researchers at the University of Utah has studied both the effects by performing molecular scale simulations, thermodynamic modeling and experiments using analytical tools. This paper discusses the experimental effect of the presence of nano pores and organic...","publisher":"Society of Petroleum Engineers","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"SPE Annual Technical Conference and Exhibition"},"translated_abstract":"The economic and increased production of oil and gas from shale plays in the United States plays a key role in the country\u0026#39;s energy independence. There are many factors that govern increased production of oil and gas from shales. One such factor is the assessment of the correct in-situ oil bubble point in shales which is critical in the optimization of hydrocarbon production. Shales are nano porous organic-rich sedimentary rocks that act as both source and reservoir oil and gas systems. The effect of nano pore confinement on the bubble point of oil in shales has been widely studied and documented in the SPE papers. However, the effect of organic matter presence on the bubble point of oil in shales has not been explored. The researchers at the University of Utah has studied both the effects by performing molecular scale simulations, thermodynamic modeling and experiments using analytical tools. This paper discusses the experimental effect of the presence of nano pores and organic...","internal_url":"https://www.academia.edu/77772846/Experimental_Verification_of_Changing_Bubble_Points_of_Oils_in_Shales_Effect_of_Preferential_Absorption_by_Kerogen_and_Confinement_of_Fluids","translated_internal_url":"","created_at":"2022-04-27T05:00:26.998-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Experimental_Verification_of_Changing_Bubble_Points_of_Oils_in_Shales_Effect_of_Preferential_Absorption_by_Kerogen_and_Confinement_of_Fluids","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":33451,"name":"Oil Shale","url":"https://www.academia.edu/Documents/in/Oil_Shale"},{"id":867776,"name":"Kerogen","url":"https://www.academia.edu/Documents/in/Kerogen"},{"id":3568454,"name":"pore confinement","url":"https://www.academia.edu/Documents/in/pore_confinement"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772845"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772845/Simplification_workflow_for_hydraulically_fractured_reservoirs"><img alt="Research paper thumbnail of Simplification workflow for hydraulically fractured reservoirs" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772845/Simplification_workflow_for_hydraulically_fractured_reservoirs">Simplification workflow for hydraulically fractured reservoirs</a></div><div class="wp-workCard_item"><span>Petroleum</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Production from unconventional formations, such as shales, has significantly increased i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Production from unconventional formations, such as shales, has significantly increased in recent years by stimulating large portions of a reservoir through the application of horizontal drilling and hydraulic fracturing. Although oil shales are heavily dependent on oil prices, production forecasts remain positive in the North-American region. Due to the complexity of hydraulically fractured tight formations, reservoir numerical simulation has become the standard tool to assess and predict production performance from these unconventional resources. Many of these unconventional fields are immense, consisting of multistage and multiwell projects, which results in impractical simulation run times. Hence, simplification of large-scale simulation models is now common both in the industry and academia. Typical simplified models such as the “single fracture” approach do not often capture the physics of large-scale projects which results in inaccurate results. In this paper we present a simple, yet rigorous workflow that generates simplified representative models in order to achieve low simulation run times while capturing physical phenomena which is fundamental for accurate calculations. The proposed workflow is based on consideration of representative portions of a large-scale model followed by post-process scaling to obtain desired full model results. The simplified models that result from the application of the proposed workflow for a single well and a multiwell case are compared to full-scale models and the “single fracture” model. Comparison of fluid rates and cumulative production show that accurate results are possible for simplified models if all important components for a particular case are taken into account. Finally, application of the workflow is shown for a heterogeneous field case where prediction studies can be carried out.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772845"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772845"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772845; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772845]").text(description); $(".js-view-count[data-work-id=77772845]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772845; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772845']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772845, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772845]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772845,"title":"Simplification workflow for hydraulically fractured reservoirs","translated_title":"","metadata":{"abstract":"Abstract Production from unconventional formations, such as shales, has significantly increased in recent years by stimulating large portions of a reservoir through the application of horizontal drilling and hydraulic fracturing. Although oil shales are heavily dependent on oil prices, production forecasts remain positive in the North-American region. Due to the complexity of hydraulically fractured tight formations, reservoir numerical simulation has become the standard tool to assess and predict production performance from these unconventional resources. Many of these unconventional fields are immense, consisting of multistage and multiwell projects, which results in impractical simulation run times. Hence, simplification of large-scale simulation models is now common both in the industry and academia. Typical simplified models such as the “single fracture” approach do not often capture the physics of large-scale projects which results in inaccurate results. In this paper we present a simple, yet rigorous workflow that generates simplified representative models in order to achieve low simulation run times while capturing physical phenomena which is fundamental for accurate calculations. The proposed workflow is based on consideration of representative portions of a large-scale model followed by post-process scaling to obtain desired full model results. The simplified models that result from the application of the proposed workflow for a single well and a multiwell case are compared to full-scale models and the “single fracture” model. Comparison of fluid rates and cumulative production show that accurate results are possible for simplified models if all important components for a particular case are taken into account. Finally, application of the workflow is shown for a heterogeneous field case where prediction studies can be carried out.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Petroleum"},"translated_abstract":"Abstract Production from unconventional formations, such as shales, has significantly increased in recent years by stimulating large portions of a reservoir through the application of horizontal drilling and hydraulic fracturing. Although oil shales are heavily dependent on oil prices, production forecasts remain positive in the North-American region. Due to the complexity of hydraulically fractured tight formations, reservoir numerical simulation has become the standard tool to assess and predict production performance from these unconventional resources. Many of these unconventional fields are immense, consisting of multistage and multiwell projects, which results in impractical simulation run times. Hence, simplification of large-scale simulation models is now common both in the industry and academia. Typical simplified models such as the “single fracture” approach do not often capture the physics of large-scale projects which results in inaccurate results. In this paper we present a simple, yet rigorous workflow that generates simplified representative models in order to achieve low simulation run times while capturing physical phenomena which is fundamental for accurate calculations. The proposed workflow is based on consideration of representative portions of a large-scale model followed by post-process scaling to obtain desired full model results. The simplified models that result from the application of the proposed workflow for a single well and a multiwell case are compared to full-scale models and the “single fracture” model. Comparison of fluid rates and cumulative production show that accurate results are possible for simplified models if all important components for a particular case are taken into account. Finally, application of the workflow is shown for a heterogeneous field case where prediction studies can be carried out.","internal_url":"https://www.academia.edu/77772845/Simplification_workflow_for_hydraulically_fractured_reservoirs","translated_internal_url":"","created_at":"2022-04-27T05:00:26.863-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Simplification_workflow_for_hydraulically_fractured_reservoirs","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":5285,"name":"Petroleum","url":"https://www.academia.edu/Documents/in/Petroleum"},{"id":30896,"name":"Reservoir Simulation","url":"https://www.academia.edu/Documents/in/Reservoir_Simulation"},{"id":897823,"name":"Elsevier","url":"https://www.academia.edu/Documents/in/Elsevier"},{"id":2590967,"name":"tight rock shale formations","url":"https://www.academia.edu/Documents/in/tight_rock_shale_formations"}],"urls":[{"id":19967110,"url":"https://api.elsevier.com/content/article/PII:S240565611730072X?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772844"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772844/Analysis_of_North_American_Tight_oil_production"><img alt="Research paper thumbnail of Analysis of North-American Tight oil production" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772844/Analysis_of_North_American_Tight_oil_production">Analysis of North-American Tight oil production</a></div><div class="wp-workCard_item"><span>AIChE Journal</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">North-American tight oil production has been on the rise due to the introduction of new drilling ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">North-American tight oil production has been on the rise due to the introduction of new drilling and hydraulic fracturing technologies. Such advances have dramatically changed the conventional understanding of the hydrocarbon recovery process. A dimensionless study of tight oil production across the United Sates in plays such as the Bakken, Niobrara, Eagle Ford, Woodford, Bone Spring, and Wolfcamp shed light on some of these recovery processes. Production from any well, regardless of geologic attributes and operating conditions, fits into a universal curve during its initial productive period. Subsequently, production becomes a strong function of hydrocarbon thermodynamics and multiphase flow. Results from this analysis help rank important parameters that affect oil recovery in terms of how wells are operated and the reservoir&#39;s intrinsic geological and fluid properties. Furthermore, production results are combined with a simple dimensionless economic analysis to determine optimal fracture configurations independent of oil price environment. © 2017 American Institute of Chemical Engineers AIChE J, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772844"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772844"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772844; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772844]").text(description); $(".js-view-count[data-work-id=77772844]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772844; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772844']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772844, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772844]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772844,"title":"Analysis of North-American Tight oil production","translated_title":"","metadata":{"abstract":"North-American tight oil production has been on the rise due to the introduction of new drilling and hydraulic fracturing technologies. Such advances have dramatically changed the conventional understanding of the hydrocarbon recovery process. A dimensionless study of tight oil production across the United Sates in plays such as the Bakken, Niobrara, Eagle Ford, Woodford, Bone Spring, and Wolfcamp shed light on some of these recovery processes. Production from any well, regardless of geologic attributes and operating conditions, fits into a universal curve during its initial productive period. Subsequently, production becomes a strong function of hydrocarbon thermodynamics and multiphase flow. Results from this analysis help rank important parameters that affect oil recovery in terms of how wells are operated and the reservoir\u0026#39;s intrinsic geological and fluid properties. Furthermore, production results are combined with a simple dimensionless economic analysis to determine optimal fracture configurations independent of oil price environment. © 2017 American Institute of Chemical Engineers AIChE J, 2017","publisher":"Wiley","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"AIChE Journal"},"translated_abstract":"North-American tight oil production has been on the rise due to the introduction of new drilling and hydraulic fracturing technologies. Such advances have dramatically changed the conventional understanding of the hydrocarbon recovery process. A dimensionless study of tight oil production across the United Sates in plays such as the Bakken, Niobrara, Eagle Ford, Woodford, Bone Spring, and Wolfcamp shed light on some of these recovery processes. Production from any well, regardless of geologic attributes and operating conditions, fits into a universal curve during its initial productive period. Subsequently, production becomes a strong function of hydrocarbon thermodynamics and multiphase flow. Results from this analysis help rank important parameters that affect oil recovery in terms of how wells are operated and the reservoir\u0026#39;s intrinsic geological and fluid properties. Furthermore, production results are combined with a simple dimensionless economic analysis to determine optimal fracture configurations independent of oil price environment. © 2017 American Institute of Chemical Engineers AIChE J, 2017","internal_url":"https://www.academia.edu/77772844/Analysis_of_North_American_Tight_oil_production","translated_internal_url":"","created_at":"2022-04-27T05:00:26.739-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Analysis_of_North_American_Tight_oil_production","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":72,"name":"Chemical Engineering","url":"https://www.academia.edu/Documents/in/Chemical_Engineering"},{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":357894,"name":"Oil Production","url":"https://www.academia.edu/Documents/in/Oil_Production"},{"id":2820942,"name":"Aiche","url":"https://www.academia.edu/Documents/in/Aiche"}],"urls":[{"id":19967109,"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Faic.16034"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772843"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772843/Effect_of_Different_Flow_Schemes_on_Heat_Recovery_from_Enhanced_Geothermal_Systems_EGS_"><img alt="Research paper thumbnail of Effect of Different Flow Schemes on Heat Recovery from Enhanced Geothermal Systems (EGS)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772843/Effect_of_Different_Flow_Schemes_on_Heat_Recovery_from_Enhanced_Geothermal_Systems_EGS_">Effect of Different Flow Schemes on Heat Recovery from Enhanced Geothermal Systems (EGS)</a></div><div class="wp-workCard_item"><span>Energy</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Operational optimization is the key to maximize the heat extraction efficiency of Enhanced Geothe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Operational optimization is the key to maximize the heat extraction efficiency of Enhanced Geothermal Systems (EGS). Injection/production flowrate is one of the operational parameters that can be easily manipulated to produce desired amount of energy. In this study, the effect of different flow schemes on the rate of heat production is analyzed over a period of 30 years. Seven flow schemes (four continuous functions namely constant flow, linear flow, exponential flow, mirror exponential flow, and three step functions with step sizes of six months, three years and ten years) developed on the basis of mathematical functions were examined. A doublet EGS model with a single fracture was simulated using a commercial thermal reservoir simulator. The reservoir and well data were obtained from the FORGE (Frontier Observatory for Research in Geothermal Energy) site at Milford Utah. The results were analyzed on the basis of their temperature decline curves for the produced water and the total amount of heat extracted over the entire period. The exponential flow scheme is the optimum case considering the rise in energy demand over the next 30 years. The amount of heat extracted per unit volume of water decreases with increase in total water volume circulated.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772843"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772843"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772843; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772843]").text(description); $(".js-view-count[data-work-id=77772843]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772843; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772843']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772843, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772843]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772843,"title":"Effect of Different Flow Schemes on Heat Recovery from Enhanced Geothermal Systems (EGS)","translated_title":"","metadata":{"abstract":"Operational optimization is the key to maximize the heat extraction efficiency of Enhanced Geothermal Systems (EGS). Injection/production flowrate is one of the operational parameters that can be easily manipulated to produce desired amount of energy. In this study, the effect of different flow schemes on the rate of heat production is analyzed over a period of 30 years. Seven flow schemes (four continuous functions namely constant flow, linear flow, exponential flow, mirror exponential flow, and three step functions with step sizes of six months, three years and ten years) developed on the basis of mathematical functions were examined. A doublet EGS model with a single fracture was simulated using a commercial thermal reservoir simulator. The reservoir and well data were obtained from the FORGE (Frontier Observatory for Research in Geothermal Energy) site at Milford Utah. The results were analyzed on the basis of their temperature decline curves for the produced water and the total amount of heat extracted over the entire period. The exponential flow scheme is the optimum case considering the rise in energy demand over the next 30 years. The amount of heat extracted per unit volume of water decreases with increase in total water volume circulated.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Energy"},"translated_abstract":"Operational optimization is the key to maximize the heat extraction efficiency of Enhanced Geothermal Systems (EGS). Injection/production flowrate is one of the operational parameters that can be easily manipulated to produce desired amount of energy. In this study, the effect of different flow schemes on the rate of heat production is analyzed over a period of 30 years. Seven flow schemes (four continuous functions namely constant flow, linear flow, exponential flow, mirror exponential flow, and three step functions with step sizes of six months, three years and ten years) developed on the basis of mathematical functions were examined. A doublet EGS model with a single fracture was simulated using a commercial thermal reservoir simulator. The reservoir and well data were obtained from the FORGE (Frontier Observatory for Research in Geothermal Energy) site at Milford Utah. The results were analyzed on the basis of their temperature decline curves for the produced water and the total amount of heat extracted over the entire period. The exponential flow scheme is the optimum case considering the rise in energy demand over the next 30 years. The amount of heat extracted per unit volume of water decreases with increase in total water volume circulated.","internal_url":"https://www.academia.edu/77772843/Effect_of_Different_Flow_Schemes_on_Heat_Recovery_from_Enhanced_Geothermal_Systems_EGS_","translated_internal_url":"","created_at":"2022-04-27T05:00:26.601-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Effect_of_Different_Flow_Schemes_on_Heat_Recovery_from_Enhanced_Geothermal_Systems_EGS_","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering"},{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":5412,"name":"Energy","url":"https://www.academia.edu/Documents/in/Energy"},{"id":51531,"name":"Pergamon","url":"https://www.academia.edu/Documents/in/Pergamon"},{"id":554780,"name":"Interdisciplinary Engineering","url":"https://www.academia.edu/Documents/in/Interdisciplinary_Engineering"},{"id":2600527,"name":"Enhanced Geothermal System (EGS)","url":"https://www.academia.edu/Documents/in/Enhanced_Geothermal_System_EGS_"}],"urls":[{"id":19967108,"url":"https://api.elsevier.com/content/article/PII:S0360544219305407?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772842"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772842/Performance_evaluation_of_enhanced_geothermal_system_EGS_Surrogate_models_sensitivity_study_and_ranking_key_parameters"><img alt="Research paper thumbnail of Performance evaluation of enhanced geothermal system (EGS): Surrogate models, sensitivity study and ranking key parameters" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772842/Performance_evaluation_of_enhanced_geothermal_system_EGS_Surrogate_models_sensitivity_study_and_ranking_key_parameters">Performance evaluation of enhanced geothermal system (EGS): Surrogate models, sensitivity study and ranking key parameters</a></div><div class="wp-workCard_item"><span>Renewable Energy</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Designing an efficient system to extract heat from an enhanced geothermal system (EGS) r...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Designing an efficient system to extract heat from an enhanced geothermal system (EGS) requires proper understanding of the behavior of the reservoir over a long period. Five key parameters namely well spacing, fracture spacing, well inclination angle, injection temperature and injection rate are considered in this study for a doublet well system. To study and evaluate the performance of an EGS, second order surrogate models for ‘produced water temperature’, at certain time intervals are developed as a function of these five factors. The in-situ properties of a candidate reservoir for designing the simulations are taken from the FORGE site, Utah. Simulations are designed using ‘Box-Behnken’ design of experiments techniques to minimize the number of simulations. The models are trained and tested with the simulated results. Fitness of the models is calculated by estimating the errors using the coefficient of determination (R2) and the normalized root mean square error (NRMSE). These surrogate models are used to study the sensitivity of the aforementioned factors on the temperature of the produced water and the heat recovery over a time period of 30 years. Finally, the hierarchy of factors, as they impact the total heat recovery are represented as a tornado plot.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772842"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772842"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772842; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772842]").text(description); $(".js-view-count[data-work-id=77772842]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772842; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772842']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772842, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772842]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772842,"title":"Performance evaluation of enhanced geothermal system (EGS): Surrogate models, sensitivity study and ranking key parameters","translated_title":"","metadata":{"abstract":"Abstract Designing an efficient system to extract heat from an enhanced geothermal system (EGS) requires proper understanding of the behavior of the reservoir over a long period. Five key parameters namely well spacing, fracture spacing, well inclination angle, injection temperature and injection rate are considered in this study for a doublet well system. To study and evaluate the performance of an EGS, second order surrogate models for ‘produced water temperature’, at certain time intervals are developed as a function of these five factors. The in-situ properties of a candidate reservoir for designing the simulations are taken from the FORGE site, Utah. Simulations are designed using ‘Box-Behnken’ design of experiments techniques to minimize the number of simulations. The models are trained and tested with the simulated results. Fitness of the models is calculated by estimating the errors using the coefficient of determination (R2) and the normalized root mean square error (NRMSE). These surrogate models are used to study the sensitivity of the aforementioned factors on the temperature of the produced water and the heat recovery over a time period of 30 years. Finally, the hierarchy of factors, as they impact the total heat recovery are represented as a tornado plot.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Renewable Energy"},"translated_abstract":"Abstract Designing an efficient system to extract heat from an enhanced geothermal system (EGS) requires proper understanding of the behavior of the reservoir over a long period. Five key parameters namely well spacing, fracture spacing, well inclination angle, injection temperature and injection rate are considered in this study for a doublet well system. To study and evaluate the performance of an EGS, second order surrogate models for ‘produced water temperature’, at certain time intervals are developed as a function of these five factors. The in-situ properties of a candidate reservoir for designing the simulations are taken from the FORGE site, Utah. Simulations are designed using ‘Box-Behnken’ design of experiments techniques to minimize the number of simulations. The models are trained and tested with the simulated results. Fitness of the models is calculated by estimating the errors using the coefficient of determination (R2) and the normalized root mean square error (NRMSE). These surrogate models are used to study the sensitivity of the aforementioned factors on the temperature of the produced water and the heat recovery over a time period of 30 years. Finally, the hierarchy of factors, as they impact the total heat recovery are represented as a tornado plot.","internal_url":"https://www.academia.edu/77772842/Performance_evaluation_of_enhanced_geothermal_system_EGS_Surrogate_models_sensitivity_study_and_ranking_key_parameters","translated_internal_url":"","created_at":"2022-04-27T05:00:26.460-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":46171107,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Performance_evaluation_of_enhanced_geothermal_system_EGS_Surrogate_models_sensitivity_study_and_ranking_key_parameters","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":46171107,"first_name":"Palash","middle_initials":null,"last_name":"Panja","page_name":"PalashPanja","domain_name":"utah","created_at":"2016-03-31T21:02:28.108-07:00","display_name":"Palash Panja","url":"https://utah.academia.edu/PalashPanja"},"attachments":[],"research_interests":[{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering"},{"id":2738,"name":"Renewable Energy","url":"https://www.academia.edu/Documents/in/Renewable_Energy"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering"},{"id":2600527,"name":"Enhanced Geothermal System (EGS)","url":"https://www.academia.edu/Documents/in/Enhanced_Geothermal_System_EGS_"}],"urls":[{"id":19967107,"url":"https://api.elsevier.com/content/article/PII:S0960148118301083?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772841"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772841/Efficient_workflow_for_simulation_of_multifractured_enhanced_geothermal_systems_EGS_"><img alt="Research paper thumbnail of Efficient workflow for simulation of multifractured enhanced geothermal systems (EGS)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772841/Efficient_workflow_for_simulation_of_multifractured_enhanced_geothermal_systems_EGS_">Efficient workflow for simulation of multifractured enhanced geothermal systems (EGS)</a></div><div class="wp-workCard_item"><span>Renewable Energy</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The increasing demand for clean energy with minimum environmental impact motivates development of...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The increasing demand for clean energy with minimum environmental impact motivates development of geothermal energy. Simulating a geothermal reservoir is complex and time consuming, mainly because of the systems spatial and temporal non-isothermal nature and the enormous size of the domain/reservoir. Simulations become even more complex when representing Enhanced Geothermal Systems (EGS), where wells in a hot, low permeability reservoir are interconnected by hydraulic fracturing to provide pathways for injection of cold water, in situ heating, and consequent production of hot water. In this study, various issues related to simulation of enhanced geothermal systems are investigated and practical solutions are proposed. A comprehensive study was conducted to show the effect of different grid systems on predictions of the transient temperature of the produced water. It is shown that the performance of an EGS is affected by the transmissivity (product of permeability and width of the fracture) of the fracture more so than by the values of permeability and width of the fracture considered individually. A simplified model (downscaled model) reduces the simulation times significantly (by 1.5–14.5 times) without compromising the accuracy of the results. In the proposed model, only two simulations - capturing small portions of the top and bottom of a reservoir with two active hydraulic fractures is used to evaluate performance of the entire reservoir. The proposed model is proved to be robust when exposed to different scenarios created by varying the inclination of the wells with respect to horizontal, spacing of the hydraulic factures, and spacing between the injection and producing wells. Value of R2 close to unity (0.96–1.0) and smaller value of MAPE (Mean Absolute Percentage Error), less than 3% in comparison to the entire reservoir simulations, indicate the utility of proposed model.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772841"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772841"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772841; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772841]").text(description); $(".js-view-count[data-work-id=77772841]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772841; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772841']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772841, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772841]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772841,"title":"Efficient workflow for simulation of multifractured enhanced geothermal systems (EGS)","translated_title":"","metadata":{"abstract":"The increasing demand for clean energy with minimum environmental impact motivates development of geothermal energy. Simulating a geothermal reservoir is complex and time consuming, mainly because of the systems spatial and temporal non-isothermal nature and the enormous size of the domain/reservoir. Simulations become even more complex when representing Enhanced Geothermal Systems (EGS), where wells in a hot, low permeability reservoir are interconnected by hydraulic fracturing to provide pathways for injection of cold water, in situ heating, and consequent production of hot water. In this study, various issues related to simulation of enhanced geothermal systems are investigated and practical solutions are proposed. A comprehensive study was conducted to show the effect of different grid systems on predictions of the transient temperature of the produced water. It is shown that the performance of an EGS is affected by the transmissivity (product of permeability and width of the fracture) of the fracture more so than by the values of permeability and width of the fracture considered individually. A simplified model (downscaled model) reduces the simulation times significantly (by 1.5–14.5 times) without compromising the accuracy of the results. In the proposed model, only two simulations - capturing small portions of the top and bottom of a reservoir with two active hydraulic fractures is used to evaluate performance of the entire reservoir. The proposed model is proved to be robust when exposed to different scenarios created by varying the inclination of the wells with respect to horizontal, spacing of the hydraulic factures, and spacing between the injection and producing wells. Value of R2 close to unity (0.96–1.0) and smaller value of MAPE (Mean Absolute Percentage Error), less than 3% in comparison to the entire reservoir simulations, indicate the utility of proposed model.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Renewable Energy"},"translated_abstract":"The increasing demand for clean energy with minimum environmental impact motivates development of geothermal energy. Simulating a geothermal reservoir is complex and time consuming, mainly because of the systems spatial and temporal non-isothermal nature and the enormous size of the domain/reservoir. Simulations become even more complex when representing Enhanced Geothermal Systems (EGS), where wells in a hot, low permeability reservoir are interconnected by hydraulic fracturing to provide pathways for injection of cold water, in situ heating, and consequent production of hot water. 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Advanced methods such as microwave assisted, ultrasound assisted, pulsed electric field assisted and enzyme assisted extractions, as well as pressurized liquid and supercritical fluid extractions are given more emphasis. The theory behind some advanced extractions methods is described. A brief review of applications of extractions from various parts of plants such as roots, fruits, seeds, leaves, vegetables, barks, etc. are provided.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772839"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772839"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772839; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77772839]").text(description); $(".js-view-count[data-work-id=77772839]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77772839; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77772839']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77772839, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77772839]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77772839,"title":"Green extraction methods of food polyphenols from vegetable materials","translated_title":"","metadata":{"abstract":"Green extraction methods are being developed using modern technology where less or no organic solvents are used to minimize environmental and health impacts and to maximize the yield of desired polyphenols by selective extraction. Advanced methods such as microwave assisted, ultrasound assisted, pulsed electric field assisted and enzyme assisted extractions, as well as pressurized liquid and supercritical fluid extractions are given more emphasis. The theory behind some advanced extractions methods is described. A brief review of applications of extractions from various parts of plants such as roots, fruits, seeds, leaves, vegetables, barks, etc. are provided.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Current Opinion in Food Science"},"translated_abstract":"Green extraction methods are being developed using modern technology where less or no organic solvents are used to minimize environmental and health impacts and to maximize the yield of desired polyphenols by selective extraction. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77772836"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77772836/Application_of_artificial_intelligence_to_forecast_hydrocarbon_production_from_shales"><img alt="Research paper thumbnail of Application of artificial intelligence to forecast hydrocarbon production from shales" class="work-thumbnail" src="https://attachments.academia-assets.com/85049246/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77772836/Application_of_artificial_intelligence_to_forecast_hydrocarbon_production_from_shales">Application of artificial intelligence to forecast hydrocarbon production from shales</a></div><div class="wp-workCard_item"><span>Petroleum</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ffcdab799290f112becf9ee691f7691b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85049246,"asset_id":77772836,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85049246/download_file?st=MTczMjc5NzE1MCw4LjIyMi4yMDguMTQ2&st=MTczMjc5NzE1MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77772836"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77772836"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77772836; 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