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Prediction of coalbed methane production based on deep learning

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  • Guo, Zixi
  • Zhao, Jinzhou
  • You, Zhenjiang
  • Li, Yongming
  • Zhang, Shu
  • Chen, Yiyu

Abstract

Coalbed methane (CBM) is a clean energy source. The prediction of CBM production is a critical step during CBM exploitation and utilization, especially for geological well selection, engineering decision making, and production management. In past attempts, CBM production prediction methods have been limited to numerical simulation and shallow neural network. Compared with numerical simulation and shallow neural network methods, deep learning has a significant advantage in its ability to process big data with multiple sources and heterogeneity. Therefore, we developed a new method of CBM production prediction based on deep learning theory. The main novelties of this method are as follows. (1) A new feature extraction method for multiscale data sources is proposed by combining convolutional autoencoder and spatial pyramid pooling. (2) The CBM production prediction model based on deep learning is established by combining the affinity propagation (AP) algorithm and the long short-term memory (LSTM) network. Application and verification show that the accuracy of our new method is higher than that of the traditional numerical simulation and shallow neural network methods.

Suggested Citation

  • Guo, Zixi & Zhao, Jinzhou & You, Zhenjiang & Li, Yongming & Zhang, Shu & Chen, Yiyu, 2021. "Prediction of coalbed methane production based on deep learning," Energy, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:energy:v:230:y:2021:i:c:s0360544221010951
    DOI: 10.1016/j.energy.2021.120847
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    Cited by:

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    4. Du, Shuyi & Wang, Jiulong & Wang, Meizhu & Yang, Jiaosheng & Zhang, Cong & Zhao, Yang & Song, Hongqing, 2023. "A systematic data-driven approach for production forecasting of coalbed methane incorporating deep learning and ensemble learning adapted to complex production patterns," Energy, Elsevier, vol. 263(PE).
    5. 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).
    6. Wang, Kai & Gong, Haoran & Wang, Gongda & Yang, Xin & Xue, Haiteng & Du, Feng & Wang, Zhie, 2024. "N2 injection to enhance gas drainage in low-permeability coal seam: A field test and the application of deep learning algorithms," Energy, Elsevier, vol. 290(C).
    7. Zhou, H.W. & Liu, Z.L. & Zhong, J.C. & Chen, B.C. & Zhao, J.W. & Xue, D.J., 2022. "NMRI online observation of coal fracture and pore structure evolution under confining pressure and axial compressive loads: A novel approach," Energy, Elsevier, vol. 261(PA).
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    9. Zhang, Baoxin & Deng, Ze & Fu, Xuehai & Yu, Kun & Zeng, Fanhua (Bill), 2023. "An experimental study on the effects of acidization on coal permeability: Implications for the enhancement of coalbed methane production," Energy, Elsevier, vol. 280(C).
    10. Min, Chao & Wen, Guoquan & Gou, Liangjie & Li, Xiaogang & Yang, Zhaozhong, 2023. "Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing," Energy, Elsevier, vol. 285(C).
    11. Fan, Lurong & Ma, Ning & Zhang, Wen, 2023. "Multi-stakeholder equilibrium-based subsidy allocation mechanism for promoting coalbed methane scale extraction-utilization," Energy, Elsevier, vol. 277(C).
    12. Wu, Han & Liang, Yan & Gao, Xiao-Zhi, 2023. "Left-right brain interaction inspired bionic deep network for forecasting significant wave height," Energy, Elsevier, vol. 278(PB).
    13. Liu, Jinyuan & Wang, Shouxi & Wei, Nan & Qiao, Weibiao & Li, Ze & Zeng, Fanhua, 2023. "A clustering-based feature enhancement method for short-term natural gas consumption forecasting," Energy, Elsevier, vol. 278(PB).
    14. Kang, Yili & Ma, Chenglin & Xu, Chengyuan & You, Lijun & You, Zhenjiang, 2023. "Prediction of drilling fluid lost-circulation zone based on deep learning," Energy, Elsevier, vol. 276(C).

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