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    • Machine learning prediction of fracture length in tight reservoirs by integrating geostress characteristics

    • “In the field of tight oil reservoir development in Huaqing Oilfield, Ordos Basin, experts have constructed a multivariate coupling model and developed a fracturing fracture length prediction system, providing theoretical methods and technical paradigms for efficient development.”
    • Pages: 1-9(2025)   

      Received：28 May 2025，

      Published Online：29 December 2025，

    • DOI： 10.13809/j.cnki.cn32-1825/te.2025253     

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  • LU Xuejiao,LI Hongchang,LI Yuzheng,et al.Machine learning prediction of fracture length in tight reservoirs by integrating geostress characteristics[J].Petroleum Reservoir Evaluation and Development, DOI： 10.13809/j.cnki.cn32-1825/te.2025253.XXXX, XX(XX): 1-9.
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YAN CHUN 中国石油杭州地质研究院
XU LIANG 中国石油杭州地质研究院
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LIU YAXIN 西南石油大学油气藏地质及开发工程全国重点实验室
QIAO YU 西南石油大学油气藏地质及开发工程全国重点实验室;中国石油冀东油田分公司储气库建设项目部
FENG QIAO 中国石油杭州地质研究院
SUN QIUFEN 中国石油杭州地质研究院
QIN JIAZHENG 西南石油大学油气藏地质及开发工程全国重点实验室

相关机构

Gas Storage Construction Project Department, PetroChina Jidong Oilfield Company
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University
PetroChina Hangzhou Research Institute of Geology
Daqing Oilfield Exploration and Development Research Institute
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