This paper introduces a comprehensive thermo-hydro-mechanical (THM) modeling framework tailored for high-temperature aquifer thermal energy storage (HT-ATES) systems. Our framework presents a novel dual-assessment approach that simultaneously evaluates thermal performance and geomechanical stability of HT-ATES systems. The framework combines advanced sensitivity analysis with multi-objective optimization to concurrently boost thermal efficiency and maintain geomechanical safety. The model simulates the cyclic injection-extraction process while capturing the interdependent effects of heat transfer, fluid flow, and mechanical stress evolution. A distance-based Generalized Sensitivity Analysis (DGSA) is applied to identify and rank the most critical parameters influencing system performance and stability, particularly in regions such as the cold well and overlying caprock. Furthermore, surrogate models constructed with eXtreme Gradient Boosting (XGBoost) facilitate a computationally efficient Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization that investigates the trade-offs between enhancing heat production and minimizing failure risks. Validation against high-fidelity simulations reveals that, compared to a benchmark model with a thermal recovery efficiency of approximately 85% and a caprock slip tendency of 34°, the optimized designs achieve around 88% efficiency and reduce the caprock slip tendency to 29°. These quantitative improvements demonstrate that the proposed framework significantly enhances both energy production and geomechanical stability, offering valuable guidance for the design of robust HT-ATES systems under fixed geological conditions.

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.

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.

With the increasing demand for mineral and alternative energy resources, as well as the gradual depletion of shallow resources, the exploitation and utilization of mineral resources and geothermal energy in deep strata is an effective way to solve the problem of resource shortage [1]. In recent years, as a new type of resource mining mode, the co-mining of deep mineral and geothermal energy has developed rapidly [2, 3]. This method can make use of the original equipment of the mine for geothermal exploitation. However, the deep co-mining system faces two significant challenges: the first is the significant uncertainty inherent in subsurface properties, while the second is the high levels of geostress and temperature associated with deep mining. These challenges are adding some constraints on the practicality of exploiting such systems and limit the feasibility of deep resource co-mining, so that modelling efforts are needed for actual risk assessment.
Consequently, we developed a Thermo-Hydro-Mechanical (THM) coupling framework for geothermal energy exploitation in deep mines using COMSOL to quantitatively characterize the temperature field of the geothermal system and predict the stress field of the mining system, considering the joint effects of large uncertainties and THM coupling. Through SGeMS, the uncertainty and spatial heterogeneity distribution of porosity are first generated. Then, the uncertainty of the hydraulic parameter [4] (permeability), mechanical parameter [5] (elastic modulus), and thermal parameter [6] (heat capacity and heat conductivity) was derived from the porosity. 500 samples were generated within a given uncertainty range, by means of Monte Carlo simulations. The spatial and temporal distributions of the temperature field of the geothermal system, and the stress field of the mining system were simulated, for each sample with COMSOL. Using the distance-based global sensitivity analysis [6], the most sensitive parameters for deep mining are identified, the heat storage capacity of the system and evolution of the maximum stress ratio are evaluated, including uncertainty.