An interpretable deep learning framework using FCT-SMOTE and BO-TabNet algorithms for reservoir water sensitivity damage prediction
Abstract This study proposes an interpretable deep learning framework to address the high-dimensional and inherently unpredictable challenges associated with oil and gas drilling and completion operations. By comparing TabNet, Tab Transformer, Hopular, and TabDDPM through computational experiments u...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-99659-5 |
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| author | Yin-bo He Ke-ming Sheng Ming-liang Du Guan-cheng Jiang Teng-fei Dong Lei Guo Bo-tao Xu |
| author_facet | Yin-bo He Ke-ming Sheng Ming-liang Du Guan-cheng Jiang Teng-fei Dong Lei Guo Bo-tao Xu |
| author_sort | Yin-bo He |
| collection | DOAJ |
| description | Abstract This study proposes an interpretable deep learning framework to address the high-dimensional and inherently unpredictable challenges associated with oil and gas drilling and completion operations. By comparing TabNet, Tab Transformer, Hopular, and TabDDPM through computational experiments under identical conditions, TabNet was selected as the optimal approach. The framework integrates Bayesian optimization (BO) with TabNet to model complex oilfield tabular datasets. Fair Cut Tree (FCT) and Synthetic Minority Over-sampling Technique (SMOTE) are incorporated to mitigate data missingness and imbalance, thereby enhancing dataset integrity and robustness. Empirical validation was conducted using 270 data entries collected from 15 distinct oil fields, specifically focusing on reservoir water sensitivity damage in natural core samples. The proposed framework exhibited superior predictive accuracy for the water sensitivity index on an independent test set, achieving a mean absolute percentage error (MAPE) of 4.4495% and a root mean square error (RMSE) of 4.05, underscoring its strong generalization capability. Moreover, this methodological approach enables a quantitative assessment of the influence of critical factors, including reservoir water salinity, initial permeability, and the mineralogical composition of rock formations, on water sensitivity predictions. This represents a significant advancement from traditional qualitative analyses to a more rigorous quantitative factor analysis, with the interpretability findings corroborating established mechanistic insights. The proposed framework offers a versatile and reliable solution for precise predictive modeling in complex drilling and completion scenarios reliant on tabular data, thereby providing a robust theoretical foundation and algorithmic support for accurate forecasting in the oil and gas industry. |
| format | Article |
| id | doaj-art-15335c7ebd5e4ee69984c1f7e5101830 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-15335c7ebd5e4ee69984c1f7e51018302025-08-20T02:03:30ZengNature PortfolioScientific Reports2045-23222025-05-0115111610.1038/s41598-025-99659-5An interpretable deep learning framework using FCT-SMOTE and BO-TabNet algorithms for reservoir water sensitivity damage predictionYin-bo He0Ke-ming Sheng1Ming-liang Du2Guan-cheng Jiang3Teng-fei Dong4Lei Guo5Bo-tao Xu6National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)China Oilfield Services LimitedChina Oilfield Services LimitedAbstract This study proposes an interpretable deep learning framework to address the high-dimensional and inherently unpredictable challenges associated with oil and gas drilling and completion operations. By comparing TabNet, Tab Transformer, Hopular, and TabDDPM through computational experiments under identical conditions, TabNet was selected as the optimal approach. The framework integrates Bayesian optimization (BO) with TabNet to model complex oilfield tabular datasets. Fair Cut Tree (FCT) and Synthetic Minority Over-sampling Technique (SMOTE) are incorporated to mitigate data missingness and imbalance, thereby enhancing dataset integrity and robustness. Empirical validation was conducted using 270 data entries collected from 15 distinct oil fields, specifically focusing on reservoir water sensitivity damage in natural core samples. The proposed framework exhibited superior predictive accuracy for the water sensitivity index on an independent test set, achieving a mean absolute percentage error (MAPE) of 4.4495% and a root mean square error (RMSE) of 4.05, underscoring its strong generalization capability. Moreover, this methodological approach enables a quantitative assessment of the influence of critical factors, including reservoir water salinity, initial permeability, and the mineralogical composition of rock formations, on water sensitivity predictions. This represents a significant advancement from traditional qualitative analyses to a more rigorous quantitative factor analysis, with the interpretability findings corroborating established mechanistic insights. The proposed framework offers a versatile and reliable solution for precise predictive modeling in complex drilling and completion scenarios reliant on tabular data, thereby providing a robust theoretical foundation and algorithmic support for accurate forecasting in the oil and gas industry.https://doi.org/10.1038/s41598-025-99659-5Deep learningIntelligent drilling and completionComplex predicationTabNetTabular data modelingSensitivity damage |
| spellingShingle | Yin-bo He Ke-ming Sheng Ming-liang Du Guan-cheng Jiang Teng-fei Dong Lei Guo Bo-tao Xu An interpretable deep learning framework using FCT-SMOTE and BO-TabNet algorithms for reservoir water sensitivity damage prediction Scientific Reports Deep learning Intelligent drilling and completion Complex predication TabNet Tabular data modeling Sensitivity damage |
| title | An interpretable deep learning framework using FCT-SMOTE and BO-TabNet algorithms for reservoir water sensitivity damage prediction |
| title_full | An interpretable deep learning framework using FCT-SMOTE and BO-TabNet algorithms for reservoir water sensitivity damage prediction |
| title_fullStr | An interpretable deep learning framework using FCT-SMOTE and BO-TabNet algorithms for reservoir water sensitivity damage prediction |
| title_full_unstemmed | An interpretable deep learning framework using FCT-SMOTE and BO-TabNet algorithms for reservoir water sensitivity damage prediction |
| title_short | An interpretable deep learning framework using FCT-SMOTE and BO-TabNet algorithms for reservoir water sensitivity damage prediction |
| title_sort | interpretable deep learning framework using fct smote and bo tabnet algorithms for reservoir water sensitivity damage prediction |
| topic | Deep learning Intelligent drilling and completion Complex predication TabNet Tabular data modeling Sensitivity damage |
| url | https://doi.org/10.1038/s41598-025-99659-5 |
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