Clastic facies classification using machine learning-based algorithms: A case study from Rawat Basin, Sudan
Machine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature (seven well logs and one facies log) were used to classify four facies. Data pre-processing and preparation involve two processes: data cleaning and feature scaling. Several...
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Energy Geoscience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666759224000684 |
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| author | Anas Mohamed Abaker Babai Olugbenga Ajayi Ehinola Omer.I.M. Fadul Abul Gebbayin Mohammed Abdalla Elsharif Ibrahim |
| author_facet | Anas Mohamed Abaker Babai Olugbenga Ajayi Ehinola Omer.I.M. Fadul Abul Gebbayin Mohammed Abdalla Elsharif Ibrahim |
| author_sort | Anas Mohamed Abaker Babai |
| collection | DOAJ |
| description | Machine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature (seven well logs and one facies log) were used to classify four facies. Data pre-processing and preparation involve two processes: data cleaning and feature scaling. Several machine learning algorithms, including Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) for classification, were tested using different iterations and various combinations of features and parameters. The support vector radial kernel training model achieved an accuracy of 72.49% without grid search and 64.02% with grid search, while the blind-well test scores were 71.01% and 69.67%, respectively. The Decision Tree (DT) Hyperparameter Optimization model showed an accuracy of 64.15% for training and 67.45% for testing. In comparison, the Decision Tree coupled with grid search yielded better results, with a training score of 69.91% and a testing score of 67.89%. The model's validation was carried out using the blind well validation approach, which achieved an accuracy of 69.81%. Three algorithms were used to generate the gradient-boosting model. During training, the Gradient Boosting classifier achieved an accuracy score of 71.57%, and during testing, it achieved 69.89%. The Grid Search model achieved a higher accuracy score of 72.14% during testing. The Extreme Gradient Boosting model had the lowest accuracy score, with only 66.13% for training and 66.12% for testing. For validation, the Gradient Boosting (GB) classifier model achieved an accuracy score of 75.41% on the blind well test, while the Gradient Boosting with Grid Search achieved an accuracy score of 71.36%. The Enhanced Random Forest and Random Forest with Bagging algorithms were the most effective, with validation accuracies of 78.30% and 79.18%, respectively. However, the Random Forest and Random Forest with Grid Search models displayed significant variance between their training and testing scores, indicating the potential for overfitting. Random Forest (RF) and Gradient Boosting (GB) are highly effective for facies classification because they handle complex relationships and provide high predictive accuracy. The choice between the two depends on specific project requirements, including interpretability, computational resources, and data nature. |
| format | Article |
| id | doaj-art-a26b1d5de56348d095fd3f61fc389fb4 |
| institution | Kabale University |
| issn | 2666-7592 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
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| series | Energy Geoscience |
| spelling | doaj-art-a26b1d5de56348d095fd3f61fc389fb42025-01-30T05:15:03ZengKeAi Communications Co., Ltd.Energy Geoscience2666-75922025-03-0161100353Clastic facies classification using machine learning-based algorithms: A case study from Rawat Basin, SudanAnas Mohamed Abaker Babai0Olugbenga Ajayi Ehinola1Omer.I.M. Fadul Abul Gebbayin2Mohammed Abdalla Elsharif Ibrahim3Pan African University Life and Earth Sciences Institute (including Health and Agriculture)(PAULESI), Nigeria; College of Petroleum Geology and Minerals, University of Bahri, Khartoum North, 11116, Sudan; Corresponding author.Department of Geology, University of Ibadan, Ibadan, Nigeria2B OPCO- E&D Dept, Al Morgan District, GNPOC Tower, Lot 91, SudanDepartment of Mineral Development and Oil and Gas Engineering, Academy of Engineering, Peoples' Friendship University of Russia (RUDN University), 117198, Moscow, RussiaMachine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature (seven well logs and one facies log) were used to classify four facies. Data pre-processing and preparation involve two processes: data cleaning and feature scaling. Several machine learning algorithms, including Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) for classification, were tested using different iterations and various combinations of features and parameters. The support vector radial kernel training model achieved an accuracy of 72.49% without grid search and 64.02% with grid search, while the blind-well test scores were 71.01% and 69.67%, respectively. The Decision Tree (DT) Hyperparameter Optimization model showed an accuracy of 64.15% for training and 67.45% for testing. In comparison, the Decision Tree coupled with grid search yielded better results, with a training score of 69.91% and a testing score of 67.89%. The model's validation was carried out using the blind well validation approach, which achieved an accuracy of 69.81%. Three algorithms were used to generate the gradient-boosting model. During training, the Gradient Boosting classifier achieved an accuracy score of 71.57%, and during testing, it achieved 69.89%. The Grid Search model achieved a higher accuracy score of 72.14% during testing. The Extreme Gradient Boosting model had the lowest accuracy score, with only 66.13% for training and 66.12% for testing. For validation, the Gradient Boosting (GB) classifier model achieved an accuracy score of 75.41% on the blind well test, while the Gradient Boosting with Grid Search achieved an accuracy score of 71.36%. The Enhanced Random Forest and Random Forest with Bagging algorithms were the most effective, with validation accuracies of 78.30% and 79.18%, respectively. However, the Random Forest and Random Forest with Grid Search models displayed significant variance between their training and testing scores, indicating the potential for overfitting. Random Forest (RF) and Gradient Boosting (GB) are highly effective for facies classification because they handle complex relationships and provide high predictive accuracy. The choice between the two depends on specific project requirements, including interpretability, computational resources, and data nature.http://www.sciencedirect.com/science/article/pii/S2666759224000684Machine learningFacies classificationGradient Boosting (GB)Support Vector Classifier (SVC)Random Forest (RF)Decision Tree (DT) |
| spellingShingle | Anas Mohamed Abaker Babai Olugbenga Ajayi Ehinola Omer.I.M. Fadul Abul Gebbayin Mohammed Abdalla Elsharif Ibrahim Clastic facies classification using machine learning-based algorithms: A case study from Rawat Basin, Sudan Energy Geoscience Machine learning Facies classification Gradient Boosting (GB) Support Vector Classifier (SVC) Random Forest (RF) Decision Tree (DT) |
| title | Clastic facies classification using machine learning-based algorithms: A case study from Rawat Basin, Sudan |
| title_full | Clastic facies classification using machine learning-based algorithms: A case study from Rawat Basin, Sudan |
| title_fullStr | Clastic facies classification using machine learning-based algorithms: A case study from Rawat Basin, Sudan |
| title_full_unstemmed | Clastic facies classification using machine learning-based algorithms: A case study from Rawat Basin, Sudan |
| title_short | Clastic facies classification using machine learning-based algorithms: A case study from Rawat Basin, Sudan |
| title_sort | clastic facies classification using machine learning based algorithms a case study from rawat basin sudan |
| topic | Machine learning Facies classification Gradient Boosting (GB) Support Vector Classifier (SVC) Random Forest (RF) Decision Tree (DT) |
| url | http://www.sciencedirect.com/science/article/pii/S2666759224000684 |
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