Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs
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- Kyoungsu Kim & Jonggeun Choe, 2019. "Hydraulic Fracture Design with a Proxy Model for Unconventional Shale Gas Reservoir with Considering Feasibility Study," Energies, MDPI, vol. 12(2), pages 1-12, January.
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- Peng, Weike & Gao, Jiaxin & Chen, Yuntian & Wang, Shengwei, 2024. "Bridging data barriers among participants: Assessing the potential of geoenergy through federated learning," Applied Energy, Elsevier, vol. 367(C).
- Bin Yang & Xin Zhu & Boan Wei & Minzhang Liu & Yifan Li & Zhihan Lv & Faming Wang, 2023. "Computer Vision and Machine Learning Methods for Heat Transfer and Fluid Flow in Complex Structural Microchannels: A Review," Energies, MDPI, vol. 16(3), pages 1-24, February.
- Fargalla, Mandella Ali M. & Yan, Wei & Deng, Jingen & Wu, Tao & Kiyingi, Wyclif & Li, Guangcong & Zhang, Wei, 2024. "TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs," Energy, Elsevier, vol. 290(C).
- Han, Sun & Zhenghao, Meng & Meilin, Li & Xiaohui, Yang & Xiaoxue, Wang, 2023. "Global supply sustainability assessment of critical metals for clean energy technology," Resources Policy, Elsevier, vol. 85(PB).
- Lixia Kang & Wei Guo & Xiaowei Zhang & Yuyang Liu & Zhaoyuan Shao, 2022. "Differentiation and Prediction of Shale Gas Production in Horizontal Wells: A Case Study of the Weiyuan Shale Gas Field, China," Energies, MDPI, vol. 15(17), pages 1-13, August.
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Keywords
shale reservoir; proxy model; data-driven; deep neural network; principal component analysis; variable importance analysis;All these keywords.
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