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A novel interpretable machine learning framework for predicting gas-bearing properties of tight sandstone reservoirs

Liu Cao Ben-Jie-Ming Liu Yong Ma Xiao-Juan Wang Zhi-Min Jin Ao-Bo Zhang Fu-Jie Jiang Zhang-Xing Chen Li-Na Huo Run-Hai Feng Di Chen Meng-Yang Wang Jian Li Yang Gao

石油科学（英文版）2026，Vol.23Issue(7)：3805-3833,29.

石油科学（英文版）2026，Vol.23Issue(7)：3805-3833,29.DOI:10.1016/j.petsci.2026.05.018

A novel interpretable machine learning framework for predicting gas-bearing properties of tight sandstone reservoirs

Liu Cao ¹Ben-Jie-Ming Liu ²Yong Ma ³Xiao-Juan Wang ⁴Zhi-Min Jin ⁴Ao-Bo Zhang ⁴Fu-Jie Jiang ³Zhang-Xing Chen ⁵Li-Na Huo ³Run-Hai Feng ⁶Di Chen ³Meng-Yang Wang ³Jian Li ⁷Yang Gao⁸

作者信息

1. College of Artificial Intelligence,China University of Petroleum(Beijing),Beijing,102249,China||State Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||Ningbo Key Laboratory of Low-Carbon Hydrogen Energy,Eastern Institute of Technology,Ningbo,315200,Zhejiang,China
2. State Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||Ningbo Key Laboratory of Low-Carbon Hydrogen Energy,Eastern Institute of Technology,Ningbo,315200,Zhejiang,China||College of Safety and Ocean Engineering,China University of Petroleum(Beijing),Beijing,102249,China
3. State Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,China
4. Research Institute of Exploration and Development,PetroChina Southwest Oil & Gas Field Company,Chengdu,610041,Sichuan,China
5. State Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||Ningbo Key Laboratory of Low-Carbon Hydrogen Energy,Eastern Institute of Technology,Ningbo,315200,Zhejiang,China||Chemical and Petroleum Engineering,Schulich School of Engineering,University of Calgary,Calgary,T2N 1N4,Canada
6. Aramco Research Center-Beijing,Aramco Asia,Beijing,100020,China
7. Ningbo Key Laboratory of Low-Carbon Hydrogen Energy,Eastern Institute of Technology,Ningbo,315200,Zhejiang,China
8. Key Laboratory of Orogenic Belts and Crustal Evolution,Ministry of Education,School of Earth and Space Sciences,Peking University,Beijing,100871,China
折叠

摘要

关键词

Tight sandstone/Gas-bearing property prediction/Interpretable machine learning framework/Semi-quantitative prediction

Key words

Tight sandstone/Gas-bearing property prediction/Interpretable machine learning framework/Semi-quantitative prediction

引用本文复制引用

Liu Cao,Ben-Jie-Ming Liu,Yong Ma,Xiao-Juan Wang,Zhi-Min Jin,Ao-Bo Zhang,Fu-Jie Jiang,Zhang-Xing Chen,Li-Na Huo,Run-Hai Feng,Di Chen,Meng-Yang Wang,Jian Li,Yang Gao..A novel interpretable machine learning framework for predicting gas-bearing properties of tight sandstone reservoirs[J].石油科学（英文版）,2026,23(7):3805-3833,29.

基金项目

This work was supported by the National Science and Tech-nology Major Project of China(No.2025ZD1400400),National Natural Science Foundation of China(NSFC)(Nos.42302142 and 42372147)and Collaborative Project between the PetroChina Southwest Oil & Gas Field Company and China University of Pe-troleum(Beijing)(HX20231362).We appreciate the Research Institute of Exploration and Development,PetroChina Southwest Oil & Gas Field Company for providing the data.We are also grateful to the reviewers for the helpful comments to improve our paper. （No.2025ZD1400400）

石油科学（英文版）

ISSN：1672-5107

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