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Well Logging Reconstruction Based on a Temporal Convolutional Network and Bidirectional Gated Recurrent Unit Network with Attention Mechanism Optimized by Improved Sand Cat Swarm Optimization

Author

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  • Guanqun Wang

    (Hebei Instrument & Meter Engineering Technology Research Center, Hebei Petroleum University of Technology, Chengde 067000, China)

  • Haibo Teng

    (Department of Computer and Information Engineering, Hebei Petroleum University of Technology, Chengde 067000, China)

  • Lei Qiao

    (Hebei Instrument & Meter Engineering Technology Research Center, Hebei Petroleum University of Technology, Chengde 067000, China)

  • Hongtao Yu

    (Department of Computer and Information Engineering, Hebei Petroleum University of Technology, Chengde 067000, China)

  • You Cui

    (Hebei Instrument & Meter Engineering Technology Research Center, Hebei Petroleum University of Technology, Chengde 067000, China)

  • Kun Xiao

    (State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China)

Abstract

Geophysical logging plays a very important role in reservoir evaluation. In the actual production process, some logging data are often missing due to well wall collapse and instrument failure. Therefore, this paper proposes a logging reconstruction method based on improved sand cat swarm optimization (ISCSO) and a temporal convolutional network (TCN) and bidirectional gated recurrent unit network with attention mechanism (BiGRU-AM). The ISCSO-TCN-BiGRU-AM can process both past and future states efficiently, thereby extracting valuable deterioration information from logging data. Firstly, the sand cat swarm optimization (SCSO) improved by the variable spiral strategy and sparrow warning mechanism is introduced. Secondly, the ISCSO’s performance is evaluated using the CEC–2022 functions and the Wilcoxon test, and the findings demonstrate that the ISCSO outperforms the rival algorithms. Finally, the logging reconstruction method based on the ISCSO-TCN-BiGRU-AM is obtained. The results are compared with the competing models, including the back propagation neural network (BPNN), GRU, and BiGRU-AM. The results show that the ISCSO-TCN-BiGRU-AM has the best performance, which verifies its high accuracy and feasibility for the missing logging reconstruction.

Suggested Citation

  • Guanqun Wang & Haibo Teng & Lei Qiao & Hongtao Yu & You Cui & Kun Xiao, 2024. "Well Logging Reconstruction Based on a Temporal Convolutional Network and Bidirectional Gated Recurrent Unit Network with Attention Mechanism Optimized by Improved Sand Cat Swarm Optimization," Energies, MDPI, vol. 17(11), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2710-:d:1407706
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    References listed on IDEAS

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    1. Niu, Dongxiao & Yu, Min & Sun, Lijie & Gao, Tian & Wang, Keke, 2022. "Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism," Applied Energy, Elsevier, vol. 313(C).
    2. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
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