Single Well Production Prediction Model of Gas Reservoir Based on CNN-BILSTM-AM
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- Zhou, Wei & Li, Xiangchengzhen & Qi, ZhongLi & Zhao, HaiHang & Yi, Jun, 2024. "A shale gas production prediction model based on masked convolutional neural network," Applied Energy, Elsevier, vol. 353(PA).
- Zha, Wenshu & Liu, Yuping & Wan, Yujin & Luo, Ruilan & Li, Daolun & Yang, Shan & Xu, Yanmei, 2022. "Forecasting monthly gas field production based on the CNN-LSTM model," Energy, Elsevier, vol. 260(C).
- 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).
- Yan, Qin & Lu, Zhiying & Liu, Hong & He, Xingtang & Zhang, Xihai & Guo, Jianlin, 2024. "Short-term prediction of integrated energy load aggregation using a bi-directional simple recurrent unit network with feature-temporal attention mechanism ensemble learning model," Applied Energy, Elsevier, vol. 355(C).
- Pan, Shaowei & Yang, Bo & Wang, Shukai & Guo, Zhi & Wang, Lin & Liu, Jinhua & Wu, Siyu, 2023. "Oil well production prediction based on CNN-LSTM model with self-attention mechanism," Energy, Elsevier, vol. 284(C).
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Keywords
production forecast; neural network; the empirical formula of gas reservoir; CNN-BILSTM; attention mechanism; Bayesian optimization;All these keywords.
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