Geothermal energy extraction through deep mine systems offers the potential to reduce the cost of geothermal systems while meeting the cooling needs of deep mines. However, the injection of cold water into the subsurface triggers strongly coupled thermo-hydro-mechanical (THM) processes that can affect the stability of underground excavations. This study evaluates the impact of geothermal energy extraction on the temperature and stability of a deep mine. By quantifying the sensitivity of the mine temperature and stability to various parameters, we propose a scheme to optimize geothermal energy production, while achieving rapid mine cooling and maintaining stability. We first evaluate the impact of geothermal operations on mine temperature and stability through THM numerical modeling. The simulations show that poro-elastic stress quickly affects mine stability, while thermal stress has a more significant impact on the long-term stability. We then use Distance-based Generalized Sensitivity Analysis (DGSA) to quantify parameter sensitivity. The analysis identifies the distance between the mine system and the geothermal system as the most influential factor. Other important parameters include the injection rate, injection temperature, well spacing, coefficient of thermal expansion, permeability, Young's modulus, and heat capacity. Finally, we propose a DGSA-based optimization framework that accounts for subsurface uncertainty and validate the optimized results. Our results indicate that, with favorable geological conditions, a rational selection of system design parameters can enhance geothermal energy production while ensuring rapid mine cooling and stability. This study provides essential insights for the optimization of deep mine geothermal systems and supports effective decision-making.

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Utilizing existing deep mining systems for geothermal extraction not only facilitates the development of geothermal systems but also helps meeting the cooling requirements for deep mining operations. In this study, a thermo-hydro-mechanical model of geothermal extraction in deep mines is developed to investigate the evolution of mine galleries stability and temperature, and the temperature changes in geothermal production wells. The uncertainty in system responses is predicted through the Bayesian Evidential Learning framework. Due to our limited understanding of the material properties and the scarcity of measurement data, uncertainties emerge in the forward simulations. Ideally, a comprehensive uncertainty analysis would be conducted to predict all possible outcomes and assess any risks. However, In light of the intractability of performing comprehensive uncertainty analyses in scenarios with vast unknown data, particularly due to the computational overhead of multiple inverse problemsolving, we employ the Bayesian Evidential Learning framework, which provides a feasible and rapid alternative for approximating prediction post-distributions and choosing the most informative data sets. Before implementing BEL, we employed Latin Hypercube Sampling to create 500 sets of realizations for forward simulations, and subsequently utilized global sensitivity analysis to evaluate the data's informational value, aiming to diminish the uncertainty in predictions. In this paper, the BEL framework is utilized to achieve two: firstly, to stochastically predict the responses of the system (stability and temperature) within the BEL framework, using machine learning to discover direct correlations between predictors (sensitive parameters) and targets (system responses). Subsequently, newly collected data can be utilized to predict the approximate posterior distributions of the corresponding gallery stability, temperature, and production well temperature, thus circumventing traditional data inversion steps. This framework can be adjusted to accommodate any predictions related to subsurface conditions; hence, our second goal involves predicting the system's long-term responses within the BEL based on shortterm data collection, forecasting posterior distributions from the acquired short-term data, and validating the efficacy of this approach. Our study indicates that in practical engineering, by (1) obtaining data of material properties and (2) key responses of short-term simulation, it is possible to predict the critical responses of the system in long-term geothermal extraction, thereby maximizing the information content of any measurement data while minimizing budget constraints and computational costs.@en