Abstract
Hot dry rock (HDR) geothermal energy, as a clean and renewable energy, has potential value in meeting the rapid demand of the social economy. Predicting the temperature distribution of a subsurface target zone is a fundamental issue for the exploration and evaluation of hot dry rock. Numerical finite−element simulation is currently the mainstream method used to study the variation in underground temperature fields. However, it has difficulty in dealing with multiple geological elements of deep and complex hot dry rock models. A Unity networking for hot dry rock temperature (HDRT−UNet) is proposed in this study that incorporates the matrix rock temperature field equation for relating the three parameters of density, specific heat capacity and thermal conductivity. According to the numerical geological structures and rock parameters of cap rocks, faults and magma intrusions, a new dataset simulated by the finite element method was created for training the HDRT−UNet. The temperature simulation results in the Gonghe basin show that the predicted temperatures within faults and granites were higher than their surrounding rocks, while a lower thermal conductivity of the cap rocks caused the temperature of overlying strata to be smaller than their surrounding temperature field. The simulation results also prove that our proposed HDRT−UNet can provide a certain evolutionary knowledge for the prediction and development of geothermal reserves.