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    • Machine learning-based coalbed methane well production prediction and fracturing parameter optimization

    • The reservoir heterogeneity of the coalbed methane block in the southern part of the Qinshui Basin is strong, making it difficult to predict the production capacity of gas wells. In addition, the lack of targeted design for fracturing construction has led to significant differences in production effects between wells after fracturing. Experts use the random forest algorithm based on multi task learning strategy to optimize fracturing parameters, providing theoretical support and practical reference for efficient development of coalbed methane in the southern part of the Qinshui Basin.
    • Vol. 15, Issue 2, Pages: 266-273(2025)   

      Received:29 August 2024

      Published:26 April 2025

    • DOI: 10.13809/j.cnki.cn32-1825/te.2025.02.011     

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  • HU Qiujia, LIU Chunchun, ZHANG Jianguo, et al. Machine learning-based coalbed methane well production prediction and fracturing parameter optimization[J]. Petroleum Reservoir Evaluation and Development, 2025, 15(2): 266-273. DOI: 10.13809/j.cnki.cn32-1825/te.2025.02.011.
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相关作者

GUO SHENGLAI 非常规油气开发教育部重点实验室中国石油大学(华东);中国石油大学(华东)石油工程学院
XIANG GANG 自然资源部复杂构造区非常规天然气评价与开发重点实验室;贵州省油气勘查开发工程研究院
LU CHANG 青岛理工大学环境与工程学院
LU TONG 非常规油气开发教育部重点实验室中国石油大学(华东);中国石油大学(华东)石油工程学院
BU YUHUAN 非常规油气开发教育部重点实验室中国石油大学(华东);中国石油大学(华东)石油工程学院
JIANG ZHIKUN 中国矿业大学(北京)地球科学与测绘工程学院
WEI YINGCHUN 中国矿业大学(北京)地球科学与测绘工程学院;中国矿业大学(北京)煤炭精细勘探与智能开发全国重点实验室
SHI HUI 中国矿业大学(北京)地球科学与测绘工程学院

相关机构

Guizhou Engineering Research Institute of Oil & Gas Exploration and Development
Key Laboratory of Unconventional Oil and Gas Development, Ministry of Education, China University of Petroleum (East China)
School of Petroleum Engineering, China University of Petroleum (East China)
School of Environmental and Municipal Engineering, Qingdao University of Technology
Key Laboratory of Unconventional Natural Gas Evaluation and Development in Complex Tectonic Regions, Ministry of Natural Resources
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