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    • Research on productivity prediction method of infilling well based on improved LSTM neural network: A case study of the middle-deep shale gas in South Sichuan

    • In the field of shale gas development in the middle and deep layers of southern Sichuan, experts have used the grey wolf optimization algorithm to optimize the long short-term memory neural network model, providing a new method for predicting the production capacity of encrypted wells.
    • Vol. 15, Issue 3, Pages: 479-487(2025)   

      Received:27 June 2024

      Published:26 June 2025

    • DOI: 10.13809/j.cnki.cn32-1825/te.2025.03.015     

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  • GUAN Wenjie, PENG Xiaolong, ZHU Suyang, et al. Research on productivity prediction method of infilling well based on improved LSTM neural network: A case study of the middle-deep shale gas in South Sichuan[J]. Petroleum Reservoir Evaluation and Development, 2025, 15(3): 479-487. DOI: 10.13809/j.cnki.cn32-1825/te.2025.03.015.
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LYU DONGLIANG 西南石油大学石油与天然气工程学院
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LI JIAN 中国石油大港油田分公司勘探开发研究院
CHENG YABIN 中国石油大港油田分公司勘探开发研究院
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Petroleum Engineering School, Southwest Petroleum University
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