A systematic data-driven approach for production forecasting of coalbed methane incorporating deep learning and ensemble learning adapted to complex production patterns
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DOI: 10.1016/j.energy.2022.126121
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Cited by:
- Song, Hongqing & Lao, Junming & Zhang, Liyuan & Xie, Chiyu & Wang, Yuhe, 2023. "Underground hydrogen storage in reservoirs: pore-scale mechanisms and optimization of storage capacity and efficiency," Applied Energy, Elsevier, vol. 337(C).
- Du, Shuyi & Wang, Meizhu & Yang, Jiaosheng & Zhao, Yang & Wang, Jiulong & Yue, Ming & Xie, Chiyu & Song, Hongqing, 2023. "An enhanced prediction framework for coalbed methane production incorporating deep learning and transfer learning," Energy, Elsevier, vol. 282(C).
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Keywords
Coalbed methane; Production forecast; Deep learning; Local outlier factor; Bi-LSTM; Xgboost;All these keywords.
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