Deep-time temperature field simulation of hot dry rock: A deep learning method in both time and space dimensions.

• Novel approach: our study proposes a 3D U-shaped neural network that can accurately simulate the temperature field of hot dry rocks in both space and time dimensions. This approach is a significant advancement as previous methods have addressed these tasks separately, presenting challenges. • Improved accuracy: by incorporating an attention mechanism and residual structure, our neural network captures the nonlinear relationship between the temperature field and rock strata parameters more accurately. This leads to more precise temperature field simulations of hot dry rocks and enhances our understanding of the system. • Practical implications: the simulated temperature field provides valuable insights into the effects of different geological formations on hot dry rocks. This information is crucial for predicting and exploiting hot dry rock geothermal reserves. Our findings lay the foundation for further research and development in this field and offer practical implications for the utilization of hot dry rock geothermal resources. Hot dry rock has emerged as a crucial renewable resource with a potential to provide significant energy to China. Accurately simulating the temperature field of hot dry rocks in space is essential in estimating their reserves. Moreover, predicting the temperature change curve of hot dry rocks over time is crucial for research in the field. Although these tasks are interrelated and share a common ground of simulating the temperature of hot dry rocks, previous approaches have dealt with them separately, each being challenging. In this study, we propose a 3D U-shaped deep-time neural network that can extract the nonlinear relationship between the temperature field and rock strata parameters, thereby enabling the simulation of the temperature field of hot dry rocks in both space and time dimensions. The proposed neural network, which incorporates an attention mechanism and residual structure, can incorporate multiple rock parameters and geological structures, leading to more accurate simulation of the temperature field of hot dry rocks. To train the network, we constructed extensive datasets based on granite rock parameters and actual geological structures, with the temperature fields simulated by the finite element method serving as labels. The resulting trained network model can successfully emulate the complex hot dry rock model, reducing the workload by combining the two networks into one. Furthermore, the simulated temperature field allows the observation of the effect of different geological activities on hot dry rocks. Network simulation has shown notable advancements in efficiency compared to traditional finite element methods. Moreover, it offers the advantage of exploring the impact of distinct geological structures on geothermal reservoirs in a spatial context. Additionally, it allows for the observation of temporal variations in the temperature distribution across different geological strata during different time periods. Thereby laying the groundwork for further prediction and exploitation of hot dry rock geothermal reserves. [ABSTRACT FROM AUTHOR]