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Identification of the formation temperature field of the southern Songliao Basin, China based on a deep belief network

Author

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  • Yang, Weifei
  • Xiao, Changlai
  • Zhang, Zhihao
  • Liang, Xiujuan

Abstract

Since geothermal energy is clean and renewable, it has attracted the attention of governments and researchers globally. However, the cost of measurement of the middle-deep formation temperature of geothermal energy is relatively high. Therefore, usually only limited measured ground temperature data are available for regional-scale geothermal research. The limited measured ground temperature data hinders obtaining a relatively accurate characterization of the regional-scale formation temperature field. This study proposes an innovative method for identifying the formation temperature field based on a deep belief network (DBN). The method was applied to identify the formation temperature field in the southern Songliao Basin, Northeast China. The initial parameters of the DBN model were first optimized through pre-training, following which the optimal parameters were obtained through fine-tuning. This allowed the method to identify the complex relationship between the 20 identification factors and the measured temperature and finally to predict the formation temperature. The optimized model showed prediction deviations ranging from −5 °C and 5 °C and from −1 °C and 1 °C for 95% and 67% of the test dataset, respectively. This research provides a reference for an optimizing the accuracy at which the three-dimensional (3D) formation temperature can be identified based on the limited measured ground temperature data. This process is also suitable for other 2D or 3D field prediction studies with limited training data, such as for the prediction of formation porosity, thermal conductivity, and permeability.

Suggested Citation

  • Yang, Weifei & Xiao, Changlai & Zhang, Zhihao & Liang, Xiujuan, 2022. "Identification of the formation temperature field of the southern Songliao Basin, China based on a deep belief network," Renewable Energy, Elsevier, vol. 182(C), pages 32-42.
    • Handle: RePEc:eee:renene:v:182:y:2022:i:c:p:32-42
    DOI: 10.1016/j.renene.2021.09.127
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    Cited by:

    1. Yongzhu Xiong & Mingyong Zhu & Yongyi Li & Kekun Huang & Yankui Chen & Jingqing Liao, 2022. "Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning," Energies, MDPI, vol. 15(8), pages 1-29, April.
    2. Zhang, Linzuo & Liang, Xiujuan & Yang, Weifei & Jia, Zilong & Xiao, Changlai & Zhang, Jiang & Dai, Rongkun & Feng, Bo & Fang, Zhang, 2025. "Identification of the formation temperature field by explainable artificial intelligence: A case study of Songyuan City, China," Energy, Elsevier, vol. 319(C).
    3. Hadavimoghaddam, Fahimeh & Amiri-Ramsheh, Behnam & Atashrouz, Saeid & Abedi, Ali & Mohaddespour, Ahmad & Ostadhassan, Mehdi & Hemmati-Sarapardeh, Abdolhossein, 2024. "Modeling CO2 loading capacity of triethanolamine (TEA) aqueous solutions via a deep learning approach," Energy, Elsevier, vol. 313(C).
    4. Wanli Gao & Jingtao Zhao & Suping Peng, 2022. "UNet–Based Temperature Simulation of Hot Dry Rock in the Gonghe Basin," Energies, MDPI, vol. 15(17), pages 1-17, August.

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