Interpretable Combinatorial Machine Learning-Based Shale Fracability Evaluation Methods
Shale gas, as an important unconventional hydrocarbon resource, has attracted much attention due to its great potential and the need for energy diversification. However, shale gas reservoirs with low permeability and low porosity pose challenges for extraction, making shale fracability evaluation cr...
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/1/186 |
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| author | Di Wang Dingyu Jiao Zihang Zhang Runze Zhou Weize Guo Huai Su |
| author_facet | Di Wang Dingyu Jiao Zihang Zhang Runze Zhou Weize Guo Huai Su |
| author_sort | Di Wang |
| collection | DOAJ |
| description | Shale gas, as an important unconventional hydrocarbon resource, has attracted much attention due to its great potential and the need for energy diversification. However, shale gas reservoirs with low permeability and low porosity pose challenges for extraction, making shale fracability evaluation crucial. Conventional methods have limitations as they cannot comprehensively consider the effects of non-linear factors or quantitatively analyse the effects of factors. In this paper, an interpretable combinatorial machine learning shale fracability evaluation method is proposed, which combines XGBoost and Bayesian optimization techniques to mine the non-linear relationship between the influencing factors and fracability, and to achieve more accurate fracability evaluations with a lower error rate (maximum MAPE not more than 20%). SHAP(SHapley Additive exPlanation) value analyses were used to quantitatively assess the factor impacts, provide the characteristic importance ranking, and visualise the contribution trend through summary and dependency plots. Analyses of seven scenarios showed that ‘Vertical—Min Horizontal’ and ‘Vertical Stress’ had the greatest impact. This approach improves the accuracy and interpretability of the assessment and provides strong support for shale gas exploration and development by enhancing the understanding of the role of factors. |
| format | Article |
| id | doaj-art-8822bfaec993413482a686e5f271d50f |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-8822bfaec993413482a686e5f271d50f2025-01-10T13:17:21ZengMDPI AGEnergies1996-10732025-01-0118118610.3390/en18010186Interpretable Combinatorial Machine Learning-Based Shale Fracability Evaluation MethodsDi Wang0Dingyu Jiao1Zihang Zhang2Runze Zhou3Weize Guo4Huai Su5State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 102200, ChinaCollege of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaCollege of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaCollege of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaCollege of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaState Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 102200, ChinaShale gas, as an important unconventional hydrocarbon resource, has attracted much attention due to its great potential and the need for energy diversification. However, shale gas reservoirs with low permeability and low porosity pose challenges for extraction, making shale fracability evaluation crucial. Conventional methods have limitations as they cannot comprehensively consider the effects of non-linear factors or quantitatively analyse the effects of factors. In this paper, an interpretable combinatorial machine learning shale fracability evaluation method is proposed, which combines XGBoost and Bayesian optimization techniques to mine the non-linear relationship between the influencing factors and fracability, and to achieve more accurate fracability evaluations with a lower error rate (maximum MAPE not more than 20%). SHAP(SHapley Additive exPlanation) value analyses were used to quantitatively assess the factor impacts, provide the characteristic importance ranking, and visualise the contribution trend through summary and dependency plots. Analyses of seven scenarios showed that ‘Vertical—Min Horizontal’ and ‘Vertical Stress’ had the greatest impact. This approach improves the accuracy and interpretability of the assessment and provides strong support for shale gas exploration and development by enhancing the understanding of the role of factors.https://www.mdpi.com/1996-1073/18/1/186shale gasfracabilitycombinatorial machine learninginterpretable |
| spellingShingle | Di Wang Dingyu Jiao Zihang Zhang Runze Zhou Weize Guo Huai Su Interpretable Combinatorial Machine Learning-Based Shale Fracability Evaluation Methods Energies shale gas fracability combinatorial machine learning interpretable |
| title | Interpretable Combinatorial Machine Learning-Based Shale Fracability Evaluation Methods |
| title_full | Interpretable Combinatorial Machine Learning-Based Shale Fracability Evaluation Methods |
| title_fullStr | Interpretable Combinatorial Machine Learning-Based Shale Fracability Evaluation Methods |
| title_full_unstemmed | Interpretable Combinatorial Machine Learning-Based Shale Fracability Evaluation Methods |
| title_short | Interpretable Combinatorial Machine Learning-Based Shale Fracability Evaluation Methods |
| title_sort | interpretable combinatorial machine learning based shale fracability evaluation methods |
| topic | shale gas fracability combinatorial machine learning interpretable |
| url | https://www.mdpi.com/1996-1073/18/1/186 |
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