A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs
Permeability is a vital parameter in reservoir engineering that affects production directly. Since this parameter's significance is obvious, finding a way for accurate determination of permeability is essential as well. In this paper, the permeability of two notable carbonate reservoirs (Ilam a...
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| Format: | Article |
| Language: | English |
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Ital Publication
2021-06-01
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| Series: | Journal of Human, Earth, and Future |
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| Online Access: | https://www.hefjournal.org/index.php/HEF/article/view/42 |
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| author | Mohsen Talebkeikhah Zahra Sadeghtabaghi Mehdi Shabani |
| author_facet | Mohsen Talebkeikhah Zahra Sadeghtabaghi Mehdi Shabani |
| author_sort | Mohsen Talebkeikhah |
| collection | DOAJ |
| description | Permeability is a vital parameter in reservoir engineering that affects production directly. Since this parameter's significance is obvious, finding a way for accurate determination of permeability is essential as well. In this paper, the permeability of two notable carbonate reservoirs (Ilam and Sarvak) in the southwest of Iran was predicted by several different methods, and the level of accuracy in all models was compared. For this purpose, Multi-Layer Perceptron Neural Network (MLP), Radial Basis Function Neural Network (RBF), Support Vector Regression (SVR), decision tree (DT), and random forest (RF) methods were chosen. The full set of real well-logging data was investigated by random forest, and five of them were selected as the potent variables. Depth, Computed gamma-ray log (CGR), Spectral gamma-ray log (SGR), Neutron porosity log (NPHI), and density log (RHOB) were considered efficacious variables and used as input data, while permeability was considered output. It should be noted that permeability values are derived from core analysis. Statistical parameters like the coefficient of determination ( ), root mean square error (RMSE) and standard deviation (SD) were determined for the train, test, and total sets. Based on statistical and graphical results, the SVM and DT models perform more accurately than others. RMSE, SD and R2values of SVM and DT models are 0.38, 1.63, 0.97 and 0.44, 2.89, and 0.96 respectively. The results of the best-proposed models of this paper were then compared with the outcome of the empirical equation for permeability prediction. The comparison indicates that artificial intelligence methods perform more accurately than traditional methods for permeability estimation, such as proposed equations.
Doi: 10.28991/HEF-2021-02-02-01
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| language | English |
| last_indexed | 2024-04-13T18:58:02Z |
| publishDate | 2021-06-01 |
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| spelling | doaj.art-2d3353f423ed46f8bcbcbc8b840a3aaa2022-12-22T02:34:10ZengItal PublicationJournal of Human, Earth, and Future2785-29972021-06-0122829910.28991/HEF-2021-02-02-0126A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon ReservoirsMohsen Talebkeikhah0Zahra Sadeghtabaghi1Mehdi Shabani2Department of Civil Engineering, École Polytechnique Fédérale de Lausanne EPFL, CH-1015 Lausanne,Department of Petroleum Engineering, Amirkabir University of Technology, Tehran,Department of Petroleum Engineering, Amirkabir University of Technology, Tehran,Permeability is a vital parameter in reservoir engineering that affects production directly. Since this parameter's significance is obvious, finding a way for accurate determination of permeability is essential as well. In this paper, the permeability of two notable carbonate reservoirs (Ilam and Sarvak) in the southwest of Iran was predicted by several different methods, and the level of accuracy in all models was compared. For this purpose, Multi-Layer Perceptron Neural Network (MLP), Radial Basis Function Neural Network (RBF), Support Vector Regression (SVR), decision tree (DT), and random forest (RF) methods were chosen. The full set of real well-logging data was investigated by random forest, and five of them were selected as the potent variables. Depth, Computed gamma-ray log (CGR), Spectral gamma-ray log (SGR), Neutron porosity log (NPHI), and density log (RHOB) were considered efficacious variables and used as input data, while permeability was considered output. It should be noted that permeability values are derived from core analysis. Statistical parameters like the coefficient of determination ( ), root mean square error (RMSE) and standard deviation (SD) were determined for the train, test, and total sets. Based on statistical and graphical results, the SVM and DT models perform more accurately than others. RMSE, SD and R2values of SVM and DT models are 0.38, 1.63, 0.97 and 0.44, 2.89, and 0.96 respectively. The results of the best-proposed models of this paper were then compared with the outcome of the empirical equation for permeability prediction. The comparison indicates that artificial intelligence methods perform more accurately than traditional methods for permeability estimation, such as proposed equations. Doi: 10.28991/HEF-2021-02-02-01 Full Text: PDFhttps://www.hefjournal.org/index.php/HEF/article/view/42permeabilitywell loggingmachine learningdata miningilam formationsarvak formation. |
| spellingShingle | Mohsen Talebkeikhah Zahra Sadeghtabaghi Mehdi Shabani A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs Journal of Human, Earth, and Future permeability well logging machine learning data mining ilam formation sarvak formation. |
| title | A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs |
| title_full | A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs |
| title_fullStr | A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs |
| title_full_unstemmed | A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs |
| title_short | A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs |
| title_sort | comparison of machine learning approaches for prediction of permeability using well log data in the hydrocarbon reservoirs |
| topic | permeability well logging machine learning data mining ilam formation sarvak formation. |
| url | https://www.hefjournal.org/index.php/HEF/article/view/42 |
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