Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs
Abstract Waterflooding is the most widely used improved oil recovery technique. Predicting the overall oil recovery resulting from waterflooding in oil reservoirs is crucial for effective reservoir management and appropriate decision-making. Machine learning (ML) techniques present resourceful and f...
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Nature Portfolio
2025-04-01
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| Online Access: | https://doi.org/10.1038/s41598-025-97235-5 |
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| author | Sayed Gomaa Ahmed Ashraf Soliman Mohamed Mansour Fares Ashraf El Salamony Khalaf G. Salem |
| author_facet | Sayed Gomaa Ahmed Ashraf Soliman Mohamed Mansour Fares Ashraf El Salamony Khalaf G. Salem |
| author_sort | Sayed Gomaa |
| collection | DOAJ |
| description | Abstract Waterflooding is the most widely used improved oil recovery technique. Predicting the overall oil recovery resulting from waterflooding in oil reservoirs is crucial for effective reservoir management and appropriate decision-making. Machine learning (ML) techniques present resourceful and fast-track tools, aiding in predicting oil recovery, which is time-consuming and costly to accomplish by simulation studies. In this paper, four machine learning models: artificial neural network (ANN), Random Forest (RF), K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) are applied to estimate the overall oil recovery (R) of water flooding. Initially, statistical methods were employed to analyze the input data before applying machine learning techniques. These models take into consideration the mobility ratio (M), reservoir permeability variation (V), water-oil production ratio (WOR), and initial water saturation (SWi). 1054 datasets were utilized to develop machine-learning models. ANN-based correlation was developed to estimate the overall oil recovery of waterflooding. The ANN proposed model achieves a high coefficient of determination (R2) of 0.999 and a low root-mean-square error (RMSE) of 0.0063 on the validation dataset. On the other hand, the other machine learning models like RF, K-NN, and SVM achieve accurate estimation of overall oil recovery (R), where the coefficients of determination (R2) values are 0.97, 0.95, and 0.80 and the RMSE scores are 0.0282, 0.0405, and 0.0629 on the validation dataset, respectively. The innovative application of such ML models demonstrates significant improvements in prediction accuracy and reliability, offering a robust solution for optimizing oil recovery processes. These machine learning models provide the industry and research with efficient and economical tools for accurately estimating oil recovery in waterflooding operations within heterogeneous reservoirs. |
| format | Article |
| id | doaj-art-84c7d7ccfac347b0939fd0653f57c22e |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-84c7d7ccfac347b0939fd0653f57c22e2025-08-20T03:14:09ZengNature PortfolioScientific Reports2045-23222025-04-0115112310.1038/s41598-025-97235-5Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirsSayed Gomaa0Ahmed Ashraf Soliman1Mohamed Mansour2Fares Ashraf El Salamony3Khalaf G. Salem4Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar UniversityPetroleum Engineering and Gas Technology Department, Faculty of Energy and Environmental Engineering, British University in Egypt (BUE)Petroleum Engineering and Gas Technology Department, Faculty of Energy and Environmental Engineering, British University in Egypt (BUE)Artificial Intelligence Department, Faculty of Informatics and Computer Science, British University in Egypt (BUE)Department of Reservoir Engineering, South Valley Egyptian Petroleum Holding Company (GANOPE)Abstract Waterflooding is the most widely used improved oil recovery technique. Predicting the overall oil recovery resulting from waterflooding in oil reservoirs is crucial for effective reservoir management and appropriate decision-making. Machine learning (ML) techniques present resourceful and fast-track tools, aiding in predicting oil recovery, which is time-consuming and costly to accomplish by simulation studies. In this paper, four machine learning models: artificial neural network (ANN), Random Forest (RF), K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) are applied to estimate the overall oil recovery (R) of water flooding. Initially, statistical methods were employed to analyze the input data before applying machine learning techniques. These models take into consideration the mobility ratio (M), reservoir permeability variation (V), water-oil production ratio (WOR), and initial water saturation (SWi). 1054 datasets were utilized to develop machine-learning models. ANN-based correlation was developed to estimate the overall oil recovery of waterflooding. The ANN proposed model achieves a high coefficient of determination (R2) of 0.999 and a low root-mean-square error (RMSE) of 0.0063 on the validation dataset. On the other hand, the other machine learning models like RF, K-NN, and SVM achieve accurate estimation of overall oil recovery (R), where the coefficients of determination (R2) values are 0.97, 0.95, and 0.80 and the RMSE scores are 0.0282, 0.0405, and 0.0629 on the validation dataset, respectively. The innovative application of such ML models demonstrates significant improvements in prediction accuracy and reliability, offering a robust solution for optimizing oil recovery processes. These machine learning models provide the industry and research with efficient and economical tools for accurately estimating oil recovery in waterflooding operations within heterogeneous reservoirs.https://doi.org/10.1038/s41598-025-97235-5WaterfloodingOil recoveryMachine learningArtificial neural networkMobility ratioReservoir permeability variation |
| spellingShingle | Sayed Gomaa Ahmed Ashraf Soliman Mohamed Mansour Fares Ashraf El Salamony Khalaf G. Salem Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs Scientific Reports Waterflooding Oil recovery Machine learning Artificial neural network Mobility ratio Reservoir permeability variation |
| title | Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs |
| title_full | Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs |
| title_fullStr | Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs |
| title_full_unstemmed | Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs |
| title_short | Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs |
| title_sort | machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs |
| topic | Waterflooding Oil recovery Machine learning Artificial neural network Mobility ratio Reservoir permeability variation |
| url | https://doi.org/10.1038/s41598-025-97235-5 |
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