What question did this study set out to answer?

The study aims to identify key factors affecting stimulation effectiveness and quantify their impact on oil production in low-permeability waterflooding reservoirs.

January 25, 2026EnergiesOpen Access

The Main Control Factors and Productivity Evaluation Method of Stimulated Well Production Based on an Interpretable Machine Learning Model

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Authors

JLJin LiChina University of Petroleum, Beijing HLHuiqing LiuChina University of Petroleum, Beijing LYLu YanHohai University

Discussion

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Implication

This research demonstrates an interpretable machine learning method to evaluate oil increment effectiveness in low-permeability reservoirs, suggesting improved productivity insights.

Key Points

The study aims to identify key factors affecting stimulation effectiveness and quantify their impact on oil production in low-permeability waterflooding reservoirs.
Analyzed geological, construction, and production data from Bai 153 Block in Changqing Oilfield.
Used Random Forest algorithm and Recursive Feature Elimination to rank factors affecting well stimulation.
Developed a multivariate quantitative evaluation model based on Pearson correlation coefficient.
Identified key controlling factors influencing oil increment post-fracturing, including geological and production parameters.
The quantitative model showed a linear correlation rate exceeding 85% between predicted and actual well test production, confirming its effectiveness.

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/6975b2aefeba4585c2d6e282 https://doi.org/10.3390/en19020548

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January 25, 2026EnergiesOpen Access

The Main Control Factors and Productivity Evaluation Method of Stimulated Well Production Based on an Interpretable Machine Learning Model

View Full Paper

Ask AI

Bookmark

Authors

JLJin LiChina University of Petroleum, Beijing HLHuiqing LiuChina University of Petroleum, Beijing LYLu YanHohai University

Discussion

Member takes

Implication

This research demonstrates an interpretable machine learning method to evaluate oil increment effectiveness in low-permeability reservoirs, suggesting improved productivity insights.

Key Points

The study aims to identify key factors affecting stimulation effectiveness and quantify their impact on oil production in low-permeability waterflooding reservoirs.
Analyzed geological, construction, and production data from Bai 153 Block in Changqing Oilfield.
Used Random Forest algorithm and Recursive Feature Elimination to rank factors affecting well stimulation.
Developed a multivariate quantitative evaluation model based on Pearson correlation coefficient.
Identified key controlling factors influencing oil increment post-fracturing, including geological and production parameters.
The quantitative model showed a linear correlation rate exceeding 85% between predicted and actual well test production, confirming its effectiveness.

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/6975b2aefeba4585c2d6e282 https://doi.org/10.3390/en19020548

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