Prediction of Formation Pressure Using Artificial Intelligence Techniques

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Using Artificial Intelligence Techniques</title> <meta name="description" content="Prediction of Formation Pressure Using Artificial Intelligence Techniques"> <meta name="keywords" content="Artificial Intelligence (AI), Formation pressure, Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machine (SVM), Functional Networks (FN), Radial Basis Function (RBF)"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> 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87359</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Prediction of Formation Pressure Using Artificial Intelligence Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulmalek%20Ahmed">Abdulmalek Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit, rotary speed, rate of penetration, mud weight, bulk density, porosity and delta sonic time. A real field data is used to predict the formation pressure using five different artificial intelligence (AI) methods such as; artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM) and functional networks (FN). All AI tools were compared with different empirical models. AI methods estimated the formation pressure by a high accuracy (high correlation coefficient and low average absolute percentage error) and outperformed all previous. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). Moreover, by comparing the AI tools with each other, the results indicate that SVM has the advantage of pore pressure prediction by its fast processing speed and high performance (a high correlation coefficient of 0.997 and a low average absolute percentage error of 0.14%). In the end, a new empirical correlation for formation pressure was developed using ANN method that can estimate pore pressure with a high precision (correlation coefficient of 0.998 and average absolute percentage error of 0.17%). <iframe src="https://publications.waset.org/abstracts/95931.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Intelligence%20%28AI%29" title="Artificial Intelligence (AI)">Artificial Intelligence (AI)</a>, <a href="https://publications.waset.org/abstracts/search?q=Formation%20pressure" title=" Formation pressure"> Formation pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Neural%20Networks%20%28ANN%29" title=" Artificial Neural Networks (ANN)"> Artificial Neural Networks (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=Fuzzy%20Logic%20%28FL%29" title=" Fuzzy Logic (FL)"> Fuzzy Logic (FL)</a>, <a href="https://publications.waset.org/abstracts/search?q=Support%20Vector%20Machine%20%28SVM%29" title=" Support Vector Machine (SVM)"> Support Vector Machine (SVM)</a>, <a href="https://publications.waset.org/abstracts/search?q=Functional%20Networks%20%28FN%29" title=" Functional Networks (FN)"> Functional Networks (FN)</a>, <a href="https://publications.waset.org/abstracts/search?q=Radial%20Basis%20Function%20%28RBF%29" title=" Radial Basis Function (RBF)"> Radial Basis Function (RBF)</a> </p> <a href="https://publications.waset.org/abstracts/95931/prediction-of-formation-pressure-using-artificial-intelligence-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95931.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> </div> </main> <footer> <div 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Prediction of Formation Pressure Using Artificial Intelligence Techniques