Neural network model for prediction of water-injection-induced energy release in deep geothermal based on extended data through numerical simulation

Wenhang Dai, Lei Zhou (), Yi Chen, Liulin Fang and Xiaocheng Li
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Wenhang Dai: Chongqing University
Lei Zhou: Chongqing University
Yi Chen: Chongqing University
Liulin Fang: Chongqing University
Xiaocheng Li: Chongqing University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 15, No 24, 17869-17894

Abstract: Abstract Energy release caused by hydraulic fracturing in enhanced geothermal systems (EGS) is a precursor to induced seismicity. This study developed an artificial neural network (ANN) model to predict cumulative seismic moment (M0) using a dataset of 972 samples obtained from numerical inversion and sensitivity analysis of critical parameters (injection volume, rate, shear stress, normal stress, friction angle, stress drop). The ANN model showed that released energy positively correlates with injection volume, rate, shear stress, and stress drop, while it negatively correlates with normal stress and friction angle. The importance of the parameters is ranked as follows: injection volume > shear stress > normal stress > friction angle > injection rate > stress drop. The ANN model demonstrates accurate predictions, with the slopes (K) of fitted lines for both original and predicted data approximating 1.0 and R2 exceeding 0.96 across all datasets: training set (K = 1.0023, R2 = 0.9981), test set (K = 1.0263, R2 = 0.9658), extrapolation test 1 (K = 0.9015, R2 = 0.9696, relative error

Keywords: EGS; Induced seismicity; Neural network; Numerical simulation; Parameter sensibility (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07495-7

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