Interpretable Combinatorial Machine Learning-Based Shale Fracability Evaluation Methods

Di Wang, Dingyu Jiao, Zihang Zhang, Runze Zhou, Weize Guo and Huai Su ()
Additional contact information
Di Wang: State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 102200, China
Dingyu Jiao: College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China
Zihang Zhang: College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China
Runze Zhou: College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China
Weize Guo: College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China
Huai Su: State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 102200, China

Energies, 2025, vol. 18, issue 1, 1-22

Abstract: 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.

Keywords: shale gas; fracability; combinatorial machine learning; interpretable (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/1/186/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/1/186/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:1:p:186-:d:1560059

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().