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Optimization of Critical Parameters of Deep Learning for Electrical Resistivity Tomography to Identifying Hydrate

Author

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  • Yang Liu

    (School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
    National Engineering Research Center of Offshore Oil and Gas Exploration, Beijing 100028, China)

  • Changchun Zou

    (School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
    National Engineering Research Center of Offshore Oil and Gas Exploration, Beijing 100028, China)

  • Qiang Chen

    (Key Laboratory of Gas Hydrate, Ministry of Natural Resources, Qingdao Institute of Marine Geology, Qingdao 266071, China
    Laboratory for Marine Mineral Resources, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266071, China)

  • Jinhuan Zhao

    (Key Laboratory of Gas Hydrate, Ministry of Natural Resources, Qingdao Institute of Marine Geology, Qingdao 266071, China
    Laboratory for Marine Mineral Resources, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266071, China)

  • Caowei Wu

    (School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
    National Engineering Research Center of Offshore Oil and Gas Exploration, Beijing 100028, China)

Abstract

As a new energy source, gas hydrates have attracted worldwide attention, but their exploration and development face enormous challenges. Thus, it has become increasingly crucial to identify hydrate distribution accurately. Electrical resistivity tomography (ERT) can be used to detect the distribution of hydrate deposits. An ERT inversion network (ERTInvNet) based on a deep neural network (DNN) is proposed, with strong learning and memory capabilities to solve the ERT nonlinear inversion problem. 160,000 samples about hydrate distribution are generated by numerical simulation, of which 10% are used for testing. The impact of different deep learning parameters (such as loss function, activation function, and optimizer) on the performance of ERT inversion is investigated to obtain a more accurate hydrate distribution. When the Logcosh loss function is enabled in ERTInvNet, the average correlation coefficient (CC) and relative error (RE) of all samples in the test sets are 0.9511 and 0.1098. The results generated by Logcosh are better than MSE, MAE, and Huber. ERTInvNet with Selu activation function can better learn the nonlinear relationship between voltage and resistivity. Its average CC and RE of all samples in the test set are 0.9449 and 0.2301, the best choices for Relu, Selu, Leaky_Relu, and Softplus. Compared with Adadelta, Adagrad, and Aadmax, Adam has the best performance in ERTInvNet with the optimizer. Its average CC and RE of all samples in the test set are 0.9449 and 0.2301, respectively. By optimizing the critical parameters of deep learning, the accuracy of ERT in identifying hydrate distribution is improved.

Suggested Citation

  • Yang Liu & Changchun Zou & Qiang Chen & Jinhuan Zhao & Caowei Wu, 2022. "Optimization of Critical Parameters of Deep Learning for Electrical Resistivity Tomography to Identifying Hydrate," Energies, MDPI, vol. 15(13), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4765-:d:851268
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    References listed on IDEAS

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    1. Sun, Yi-Fei & Zhong, Jin-Rong & Chen, Guang-Jin & Cao, Bo-Jian & Li, Rui & Chen, Dao-Yi, 2021. "A new approach to efficient and safe gas production from unsealed marine hydrate deposits," Applied Energy, Elsevier, vol. 282(PB).
    2. Jinhuan Zhao & Changling Liu & Qiang Chen & Changchun Zou & Yang Liu & Qingtao Bu & Jiale Kang & Qingguo Meng, 2022. "Experimental Investigation into Three-Dimensional Spatial Distribution of the Fracture-Filling Hydrate by Electrical Property of Hydrate-Bearing Sediments," Energies, MDPI, vol. 15(10), pages 1-12, May.
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    1. Francesco Grimaccia & Marco Montini & Alessandro Niccolai & Silvia Taddei & Silvia Trimarchi, 2022. "A Machine Learning-Based Method for Modelling a Proprietary SO 2 Removal System in the Oil and Gas Sector," Energies, MDPI, vol. 15(23), pages 1-15, December.

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