Ensemble Learning Based Sustainable Approach to Carbonate Reservoirs Permeability Prediction

Dhiaa A. Musleh, Sunday O. Olatunji, Abdulmalek A. Almajed, Ayman S. Alghamdi, Bassam K. Alamoudi, Fahad S. Almousa, Rayan A. Aleid, Saeed K. Alamoudi, Farmanullah Jan, Khansa A. Al-Mofeez and Atta Rahman ()
Additional contact information
Dhiaa A. Musleh: Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Sunday O. Olatunji: Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Abdulmalek A. Almajed: Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Ayman S. Alghamdi: Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Bassam K. Alamoudi: Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Fahad S. Almousa: Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Rayan A. Aleid: Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Saeed K. Alamoudi: Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Farmanullah Jan: Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Khansa A. Al-Mofeez: Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Atta Rahman: Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia

Sustainability, 2023, vol. 15, issue 19, 1-15

Abstract: Permeability is a crucial property that can be used to indicate whether a material can hold fluids or not. Predicting the permeability of carbonate reservoirs is always a challenging and expensive task while using traditional techniques. Traditional methods often demand a significant amount of time, resources, and manpower, which are sometimes beyond the limitations of under developing countries. However, predicting permeability with precision is crucial to characterize hydrocarbon deposits and explore oil and gas successfully. To contribute to this regard, the current study offers some permeability prediction models centered around ensemble machine learning techniques, e.g., the gradient boost (GB), random forest (RF), and a few others. In this regard, the prediction accuracy of these schemes has significantly been enhanced using feature selection and ensemble techniques. Importantly, the authors utilized actual industrial datasets in this study while evaluating the proposed models. These datasets were gathered from five different oil wells (OWL) in the Middle Eastern region when a petroleum exploration campaign was conducted. After carrying out exhaustive simulations on these datasets using ensemble learning schemes, with proper tuning of the hyperparameters, the resultant models achieved very promising results. Among the numerous tested models, the GB- and RF-based algorithms offered relatively better performance in terms of root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2 ) while predicting permeability of the carbonate reservoirs. The study can potentially be helpful for the oil and gas industry in terms of permeability prediction in carbonate reservoirs.

Keywords: permeability prediction; AdaBoost; random forest; feature selection; carbonate reservoir; oil and gas (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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