Characterization of Lithfacies Properties of Carbonate Reservoir rocks using Machine Learning Techniques
hamada, G., M. Al-khudafi, A., A. Al-Sharifi, H., A. Farea, I., O. Baarimah, S., Al‑Gathe, A., A. Bamaga, M. (2024). Characterization of Lithfacies Properties of Carbonate Reservoir rocks using Machine Learning Techniques. EKB Journal Management System, 25(2), 77-86. doi: 10.21608/jpme.2024.265484.1190
Ghareb hamada; Abbas M. Al-khudafi; Hamzah A. Al-Sharifi; Ibrahim A. Farea; Salem O. Baarimah; Abdelrigeeb Al‑Gathe; Mohamed A. Bamaga. "Characterization of Lithfacies Properties of Carbonate Reservoir rocks using Machine Learning Techniques". EKB Journal Management System, 25, 2, 2024, 77-86. doi: 10.21608/jpme.2024.265484.1190
hamada, G., M. Al-khudafi, A., A. Al-Sharifi, H., A. Farea, I., O. Baarimah, S., Al‑Gathe, A., A. Bamaga, M. (2024). 'Characterization of Lithfacies Properties of Carbonate Reservoir rocks using Machine Learning Techniques', EKB Journal Management System, 25(2), pp. 77-86. doi: 10.21608/jpme.2024.265484.1190
hamada, G., M. Al-khudafi, A., A. Al-Sharifi, H., A. Farea, I., O. Baarimah, S., Al‑Gathe, A., A. Bamaga, M. Characterization of Lithfacies Properties of Carbonate Reservoir rocks using Machine Learning Techniques. EKB Journal Management System, 2024; 25(2): 77-86. doi: 10.21608/jpme.2024.265484.1190

Characterization of Lithfacies Properties of Carbonate Reservoir rocks using Machine Learning Techniques

Journal of Petroleum and Mining Engineering
Volume 25, Issue 2 - Serial Number 1, March 2024, Page 77-86  XML PDF (884.45 K)
Document Type: Full-length article
DOI: 10.21608/jpme.2024.265484.1190
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Authors
Ghareb hamada email 1; Abbas M. Al-khudafi2; Hamzah A. Al-Sharifi2; Ibrahim A. Farea3; Salem O. Baarimah2; Abdelrigeeb Al‑Gathe2; Mohamed A. Bamaga2
1Arab Academy for Science, Technology & Maritime Transport, Alexandria, Egypt
2Hadhramout University, Al-Mukalla, Yemen
3Emirates International University, Sanaa, Yemen
Abstract
This study aims to assess the effectiveness of several decision tree machine techniques for identifying formation lithology of complex carbonate reservoir rocks in Gamal oil field. A total of 20966 log data points from four wells were used to create the study's data. Lithology is determined using seven log parameters. The seven log parameters are the density log, neutron log, sonic log, gamma ray log, deep lateral log, shallow lateral log, and resistivity log. Different decision tree-based algorithms for classification approaches were applied. Several typical machine learning models, namely the, Random Forest. Random trees, J48, reduced-error pruning decision trees, logistic model trees, Hoeffding Tree were assessed using well logging data for formation lithology prediction. The obtained results show that the random forest model, out of the proposed decision tree models, performed best at lithology identification, with precession, recall, and F-score values of 0.913, 0.914, and 0.913, respectively. Random trees came next. with average precision, recall, and F1-score of 0.837, 0.84, and 0.837, respectively, the J48 model came in third place. The Hoeffding Tree classification model, however, showed the worst performance. We conclude that boosting strategies enhance the performance of tree-based models. Evaluation of prediction capability of models is also carried out using different datasets.
Keywords
Decision tree; Lithology prediction; Machine learning; Well logging; Evaluation
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