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Evaluation of Seismicity Induced by Geothermal Development Based on Artificial Neural Network

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  • Kun Shan

    (School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
    Power China Huadong Engineering Corporation Limited, Hangzhou 311122, China
    College of Construction Engineering, Jilin University, Changchun 130026, China)

  • Yanhao Zheng

    (College of Construction Engineering, Jilin University, Changchun 130026, China)

  • Wanqiang Cheng

    (Power China Huadong Engineering Corporation Limited, Hangzhou 311122, China)

  • Zhigang Shan

    (Power China Huadong Engineering Corporation Limited, Hangzhou 311122, China)

  • Yanjun Zhang

    (College of Construction Engineering, Jilin University, Changchun 130026, China)

Abstract

The process of geothermal energy development may cause induced seismic activities, posing a potential threat to the sustainable utilization and safety of geothermal energy. To effectively evaluate the danger of induced seismic activities, this paper establishes an artificial neural network model and selects nine influencing factors as the input parameters of the neurons. Based on the results of induced seismic activity under different parameter conditions, a sensitivity analysis is conducted for each parameter, and the influence degree of each parameter on the magnitude of induced seismic activity is ranked from largest to smallest as follows: in situ stress state, fault presence or absence, depth, degree of fracture aggregation, maximum in situ stress, distance to fault, injection volume, fracture dip angle, angle between fracture, and fault. Then, the weights of each parameter in the model are modified to improve the accuracy of the model. Finally, through data collection and the literature review, the Pohang EGS project in South Korea is analyzed, and the induced seismic activity influencing factors of the Pohang EGS site are analyzed and evaluated using the induced seismic activity evaluation model. The results show that the induced seismicity are all located below 3.7 km (drilling depth). As the depth increases, the seismicity magnitude also shows a gradually increasing trend. An increase in injection volume and a shortening of the distance from faults will also lead to an increase in the seismicity magnitude. When the injection volume approaches 10,000 cubic meters, the intensity of the seismic activity sharply increases, and the maximum magnitude reaches 5.34, which is consistent with the actual situation. This model can be used for the induced seismic evaluation of future EGS projects and provide a reference for project site selection and induced seismic risk warning.

Suggested Citation

  • Kun Shan & Yanhao Zheng & Wanqiang Cheng & Zhigang Shan & Yanjun Zhang, 2025. "Evaluation of Seismicity Induced by Geothermal Development Based on Artificial Neural Network," Energies, MDPI, vol. 18(15), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:15:p:4004-:d:1711487
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    References listed on IDEAS

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    1. Liang, Xu & Xu, Tianfu & Chen, Jingyi & Jiang, Zhenjiao, 2023. "A deep-learning based model for fracture network characterization constrained by induced micro-seismicity and tracer test data in enhanced geothermal system," Renewable Energy, Elsevier, vol. 216(C).
    2. Kun Shan & Yanjun Zhang & Yanhao Zheng & Liangzhen Li & Hao Deng, 2020. "Risk Assessment of Fracturing Induced Earthquake in the Qiabuqia Geothermal Field, China," Energies, MDPI, vol. 13(22), pages 1-24, November.
    3. Gaucher, Emmanuel & Schoenball, Martin & Heidbach, Oliver & Zang, Arno & Fokker, Peter A. & van Wees, Jan-Diederik & Kohl, Thomas, 2015. "Induced seismicity in geothermal reservoirs: A review of forecasting approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1473-1490.
    4. Matteo Picozzi & Antonio Giovanni Iaccarino, 2021. "Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network," Forecasting, MDPI, vol. 3(1), pages 1-20, January.
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