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, Pages 77-86 PDF (1012.86 K) | ||
| Document Type: Full-length article | ||
| DOI: 10.21608/jpme.2024.265484.1190 | ||
| Authors | ||
| Ghareb hamada* 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 | ||
|
Statistics Article View: 346 PDF Download: 190 |
||
