Fracture distribution plays a significant role in the behavior of subsurface environments, affecting such activities as geothermal production, exploitation and management of groundwater resources, and long-term storage of nuclear waste and carbon dioxide. A key challenge in these and other applications is to estimate the fracture network properties from sparse and noisy observations. We evaluate the utility of cross-borehole thermal experiments for this task, using both physics-based particle-tracking (PBPT) heat-transfer approach and its deep neural network (DNN) surrogates. Synthetic data are provided by the PBPT simulations and used to train and test the DNN surrogates over a full range of the fracture network properties. We propose regionalized and step-by-step training techniques to reduce the computational cost of expensive PBPT forward solves over large ranges of the (to-be-estimated) parameters. Our numerical experiments suggest the feasibility of training a regionalized DNN surrogate over parameter ranges for which the PBPT solves are fast and extrapolating its predictions to parameter ranges with few additional data. We analyze the balance between computational cost and model accuracy, and provide both PBPT and DNN models for applications to others kinds of data.@en

The multi-physics coupling process during the heat extraction from enhanced geothermal system, encompassing thermo(T)-hydro(H)-mechanical(M)-chemical(C) interactions, plays a pivotal role in changing geothermal reservoir characteristics. However, a comprehensive quantitative assessment of these multi-physics behaviors has been lacking. In this study, a novel approach was proposed to calculate the magnitude of mechanical, chemical, strong mechanical-chemical coupling, and weak mechanical-chemical coupling effects on the variations of reservoir characteristics. In particular, mechanical-chemical coupling effects are quantified for the first time. They are obtained by the fracture aperture difference results across five distinct coupling models (thermo-hydro, thermo-hydro-chemical, thermo-hydro-mechanical, partially-coupled four-field, and fully-coupled four-field models). The findings indicate that mechanical effects lead to an increase in fracture aperture, while chemical effects contribute to its reduction under underbalanced injection conditions. Strong mechanical-chemical coupling effects, exhibiting a negative correlation with chemical effects, conversely result in a diminished fracture aperture. The influences of these effects are investigated from the temporal and spatial perspectives. Temporally, mechanical effects dominate early production while chemical effects become prominent in later stages. Spatially, there mainly exists two zones when stable production: a mechanical-controlled region surrounding injection wells, and a chemical-controlled area distant from the injection wells. Furthermore, sensitivity analysis of injection concentration indicates its alternation changes the reservoir traits and production performance by modifying the magnitudes of chemical and mechanical-chemical coupling effects. This quantification of multi-physics effects offers insights into optimizing injection strategies for better geothermal development. The approach could hold promising potential in other geo-energy scenarios like carbon and hydrogen storage in reservoirs.@en

As a kind of clean renewable energy, the production and utilization of geothermal resources can make a great contribution to optimizing the energy structure and energy conservation and emission reduction. The circulating heat extraction process of working fluid will disturb the equilibrium state of physical and chemical fields inside the reservoir, and involve the mutual coupling of heat transfer, flow, stress, and chemical reaction. Revealing the coupling mechanism of flow and heat transfer inside the reservoir during geothermal exploitation can provide important theoretical support for the efficient exploitation of geothermal resources. This paper reviews the research advances of the multi-field coupling model in the reservoir during geothermal production over the past 40 years. The thrust of this paper is on objective analysis and evaluation of the importance of each coupling process and its influence on reservoir heat extraction performance. Finally, we discuss the existing challenges and perspectives to promote the future development of the geothermal reservoir multi-field coupling model. An accurate understanding of the multi-field coupling mechanism, an efficient cross-scale modeling method, as well as the accurate characterization of reservoir fracture morphology, are crucial for the multi-field coupling model of geothermal production.@en

As a kind of clean renewable energy, the production and utilization of geothermal resources can make a great contribution to optimizing the energy structure and energy conservation and emission reduction. The circulating heat extraction process of working fluid will disturb the equilibrium state of physical and chemical fields inside the reservoir, and involve the mutual coupling of heat transfer, flow, stress, and chemical reaction. Revealing the coupling mechanism of flow and heat transfer inside the reservoir during geothermal exploitation can provide important theoretical support for the efficient exploitation of geothermal resources. This paper reviews the research advances of the multi-field coupling model in the reservoir during geothermal production over the past 40 years. The thrust of this paper is on objective analysis and evaluation of the importance of each coupling process and its influence on reservoir heat extraction performance. Finally, we discuss the existing challenges and perspectives to promote the future development of the geothermal reservoir multi-field coupling model. An accurate understanding of the multi-field coupling mechanism, an efficient cross-scale modeling method, as well as the accurate characterization of reservoir fracture morphology, are crucial for the multi-field coupling model of geothermal production.@en

Geothermal energy is gaining attention as an environmentally friendly and sustainable alternative to fossil fuels. There exist complicated coupling processes among fluid flow, heat transfer, mechanical deformation, and chemical reaction in fractured geothermal reservoirs. Understanding of thermo-hydro-mechanical-chemical (THMC) coupled process is of significance for the efficient development of enhanced geothermal systems. This research aims to quantify the contributions of mechanical and chemical behaviors to reservoir feature variation during geothermal production. Herein, a fully coupled THMC model is developed for the production of an enhanced geothermal system based on fracture aperture alternation. The key improvement lies in the inclusion of mechanical-chemical (MC) coupling where chemical behaviors cause stress to be redistributed. On a geographical and temporal scale, the unique proportion technique is employed to study ratios of mechanical to chemical effects and poroelasticity to thermoelasticity. The findings emphasize the necessity of incorporating the MC coupling when analyzing reservoir characteristic variations. The mechanical effect plays a major role near the injection well and in a short term. The chemical effect is dominated far from the injection well and its time scale of action is long-term. The intensity of thermoelasticity is stronger than that of poroelasticity. Moreover, the proportion analysis reveals that when stable production is achieved, there are primarily thermo-driven and chemical-driven regions. To improve production performance and adjust geothermal reservoir features, injection temperature and solute concentration should be optimized. This paper serves as a valuable reference for both the theoretical multi-physical coupling model and practical geothermal production.@en

Geothermal energy is gaining attention as an environmentally friendly and sustainable alternative to fossil fuels. There exist complicated coupling processes among fluid flow, heat transfer, mechanical deformation, and chemical reaction in fractured geothermal reservoirs. Understanding of thermo-hydro-mechanical-chemical (THMC) coupled process is of significance for the efficient development of enhanced geothermal systems. This research aims to quantify the contributions of mechanical and chemical behaviors to reservoir feature variation during geothermal production. Herein, a fully coupled THMC model is developed for the production of an enhanced geothermal system based on fracture aperture alternation. The key improvement lies in the inclusion of mechanical-chemical (MC) coupling where chemical behaviors cause stress to be redistributed. On a geographical and temporal scale, the unique proportion technique is employed to study ratios of mechanical to chemical effects and poroelasticity to thermoelasticity. The findings emphasize the necessity of incorporating the MC coupling when analyzing reservoir characteristic variations. The mechanical effect plays a major role near the injection well and in a short term. The chemical effect is dominated far from the injection well and its time scale of action is long-term. The intensity of thermoelasticity is stronger than that of poroelasticity. Moreover, the proportion analysis reveals that when stable production is achieved, there are primarily thermo-driven and chemical-driven regions. To improve production performance and adjust geothermal reservoir features, injection temperature and solute concentration should be optimized. This paper serves as a valuable reference for both the theoretical multi-physical coupling model and practical geothermal production.@en

Geothermal energy is gaining attention as an environmentally friendly and sustainable alternative to fossil fuels. There exist complicated coupling processes among fluid flow, heat transfer, mechanical deformation, and chemical reaction in fractured geothermal reservoirs. Understanding of thermo-hydro-mechanical-chemical (THMC) coupled process is of significance for the efficient development of enhanced geothermal systems. This research aims to quantify the contributions of mechanical and chemical behaviors to reservoir feature variation during geothermal production. Herein, a fully coupled THMC model is developed for the production of an enhanced geothermal system based on fracture aperture alternation. The key improvement lies in the inclusion of mechanical-chemical (MC) coupling where chemical behaviors cause stress to be redistributed. On a geographical and temporal scale, the unique proportion technique is employed to study ratios of mechanical to chemical effects and poroelasticity to thermoelasticity. The findings emphasize the necessity of incorporating the MC coupling when analyzing reservoir characteristic variations. The mechanical effect plays a major role near the injection well and in a short term. The chemical effect is dominated far from the injection well and its time scale of action is long-term. The intensity of thermoelasticity is stronger than that of poroelasticity. Moreover, the proportion analysis reveals that when stable production is achieved, there are primarily thermo-driven and chemical-driven regions. To improve production performance and adjust geothermal reservoir features, injection temperature and solute concentration should be optimized. This paper serves as a valuable reference for both the theoretical multi-physical coupling model and practical geothermal production.@en

Geothermal energy is gaining attention as an environmentally friendly and sustainable alternative to fossil fuels. There exist complicated coupling processes among fluid flow, heat transfer, mechanical deformation, and chemical reaction in fractured geothermal reservoirs. Understanding of thermo-hydro-mechanical-chemical (THMC) coupled process is of significance for the efficient development of enhanced geothermal systems. This research aims to quantify the contributions of mechanical and chemical behaviors to reservoir feature variation during geothermal production. Herein, a fully coupled THMC model is developed for the production of an enhanced geothermal system based on fracture aperture alternation. The key improvement lies in the inclusion of mechanical-chemical (MC) coupling where chemical behaviors cause stress to be redistributed. On a geographical and temporal scale, the unique proportion technique is employed to study ratios of mechanical to chemical effects and poroelasticity to thermoelasticity. The findings emphasize the necessity of incorporating the MC coupling when analyzing reservoir characteristic variations. The mechanical effect plays a major role near the injection well and in a short term. The chemical effect is dominated far from the injection well and its time scale of action is long-term. The intensity of thermoelasticity is stronger than that of poroelasticity. Moreover, the proportion analysis reveals that when stable production is achieved, there are primarily thermo-driven and chemical-driven regions. To improve production performance and adjust geothermal reservoir features, injection temperature and solute concentration should be optimized. This paper serves as a valuable reference for both the theoretical multi-physical coupling model and practical geothermal production.@en

Geothermal energy is gaining attention as an environmentally friendly and sustainable alternative to fossil fuels. There exist complicated coupling processes among fluid flow, heat transfer, mechanical deformation, and chemical reaction in fractured geothermal reservoirs. Understanding of thermo-hydro-mechanical-chemical (THMC) coupled process is of significance for the efficient development of enhanced geothermal systems. This research aims to quantify the contributions of mechanical and chemical behaviors to reservoir feature variation during geothermal production. Herein, a fully coupled THMC model is developed for the production of an enhanced geothermal system based on fracture aperture alternation. The key improvement lies in the inclusion of mechanical-chemical (MC) coupling where chemical behaviors cause stress to be redistributed. On a geographical and temporal scale, the unique proportion technique is employed to study ratios of mechanical to chemical effects and poroelasticity to thermoelasticity. The findings emphasize the necessity of incorporating the MC coupling when analyzing reservoir characteristic variations. The mechanical effect plays a major role near the injection well and in a short term. The chemical effect is dominated far from the injection well and its time scale of action is long-term. The intensity of thermoelasticity is stronger than that of poroelasticity. Moreover, the proportion analysis reveals that when stable production is achieved, there are primarily thermo-driven and chemical-driven regions. To improve production performance and adjust geothermal reservoir features, injection temperature and solute concentration should be optimized. This paper serves as a valuable reference for both the theoretical multi-physical coupling model and practical geothermal production.@en

Geothermal energy is gaining attention as an environmentally friendly and sustainable alternative to fossil fuels. There exist complicated coupling processes among fluid flow, heat transfer, mechanical deformation, and chemical reaction in fractured geothermal reservoirs. Understanding of thermo-hydro-mechanical-chemical (THMC) coupled process is of significance for the efficient development of enhanced geothermal systems. This research aims to quantify the contributions of mechanical and chemical behaviors to reservoir feature variation during geothermal production. Herein, a fully coupled THMC model is developed for the production of an enhanced geothermal system based on fracture aperture alternation. The key improvement lies in the inclusion of mechanical-chemical (MC) coupling where chemical behaviors cause stress to be redistributed. On a geographical and temporal scale, the unique proportion technique is employed to study ratios of mechanical to chemical effects and poroelasticity to thermoelasticity. The findings emphasize the necessity of incorporating the MC coupling when analyzing reservoir characteristic variations. The mechanical effect plays a major role near the injection well and in a short term. The chemical effect is dominated far from the injection well and its time scale of action is long-term. The intensity of thermoelasticity is stronger than that of poroelasticity. Moreover, the proportion analysis reveals that when stable production is achieved, there are primarily thermo-driven and chemical-driven regions. To improve production performance and adjust geothermal reservoir features, injection temperature and solute concentration should be optimized. This paper serves as a valuable reference for both the theoretical multi-physical coupling model and practical geothermal production.@en

Operational parameters optimization is of great significance to improve overall heat extraction performance from the hydrothermal or enhanced geothermal system. Injection flowrate, injection temperature, and production pressure are several of the easily human-controlled parameters to make the best of limited geothermal resources during the planned life. The net heat power and flow impedance are two contradictory production indexes to be optimized for efficient exploitation of long-term geothermal production. In this study, an integrated approach of finite element, multiple regression, non-dominated sorting genetic algorithm, and the technique for order preference by similarity to ideal solution is proposed and applied to the multilateral-well system to realize the optimization of geothermal development. Firstly, parametric cases coupling with thermal and hydraulic models are analyzed. Then, multiple regression is employed to obtain the net heat power and flow impedance functions considering human-controlled operational parameters and reservoir physical properties. Afterward, the multi-objective optimization algorithm is used to gain the Pareto solution set of injection and production parameters. Finally, the technique for order preference by similarity to ideal solution is employed to select the optimal combination of operational parameters for geothermal extraction. It is concluded that water loss and thermal drawdown are necessarily considered in the optimization process. The proposed approach represents global optimization. Operational parameters for the optimal case are (Qin, Tin, pout) which equal (49.98 °C, 62.21 kg/s, 27.44 MPa) under the conditions of this study. From the comparison between the base case and optimal case, it is observed that horizontal spread length, 2D swept area and 3D swept volume of the low-temperature scope is reduced by 77.9 m, 10 × 104 m2, and 16 × 106 m3. Besides, the thermal breakthrough has been delayed by 1.1 years. The water loss decreases by 36.23%. The optimal case demonstrates a great improvement in production performance. Sustainable exploitation is achieved through multi-objective optimization for operational parameters. The proposed method reflects superiority, efficiency, and intelligence in geothermal development.@en

Operational parameters optimization is of great significance to improve overall heat extraction performance from the hydrothermal or enhanced geothermal system. Injection flowrate, injection temperature, and production pressure are several of the easily human-controlled parameters to make the best of limited geothermal resources during the planned life. The net heat power and flow impedance are two contradictory production indexes to be optimized for efficient exploitation of long-term geothermal production. In this study, an integrated approach of finite element, multiple regression, non-dominated sorting genetic algorithm, and the technique for order preference by similarity to ideal solution is proposed and applied to the multilateral-well system to realize the optimization of geothermal development. Firstly, parametric cases coupling with thermal and hydraulic models are analyzed. Then, multiple regression is employed to obtain the net heat power and flow impedance functions considering human-controlled operational parameters and reservoir physical properties. Afterward, the multi-objective optimization algorithm is used to gain the Pareto solution set of injection and production parameters. Finally, the technique for order preference by similarity to ideal solution is employed to select the optimal combination of operational parameters for geothermal extraction. It is concluded that water loss and thermal drawdown are necessarily considered in the optimization process. The proposed approach represents global optimization. Operational parameters for the optimal case are (Qin, Tin, pout) which equal (49.98 °C, 62.21 kg/s, 27.44 MPa) under the conditions of this study. From the comparison between the base case and optimal case, it is observed that horizontal spread length, 2D swept area and 3D swept volume of the low-temperature scope is reduced by 77.9 m, 10 × 104 m2, and 16 × 106 m3. Besides, the thermal breakthrough has been delayed by 1.1 years. The water loss decreases by 36.23%. The optimal case demonstrates a great improvement in production performance. Sustainable exploitation is achieved through multi-objective optimization for operational parameters. The proposed method reflects superiority, efficiency, and intelligence in geothermal development.@en

Operational parameters optimization is of great significance to improve overall heat extraction performance from the hydrothermal or enhanced geothermal system. Injection flowrate, injection temperature, and production pressure are several of the easily human-controlled parameters to make the best of limited geothermal resources during the planned life. The net heat power and flow impedance are two contradictory production indexes to be optimized for efficient exploitation of long-term geothermal production. In this study, an integrated approach of finite element, multiple regression, non-dominated sorting genetic algorithm, and the technique for order preference by similarity to ideal solution is proposed and applied to the multilateral-well system to realize the optimization of geothermal development. Firstly, parametric cases coupling with thermal and hydraulic models are analyzed. Then, multiple regression is employed to obtain the net heat power and flow impedance functions considering human-controlled operational parameters and reservoir physical properties. Afterward, the multi-objective optimization algorithm is used to gain the Pareto solution set of injection and production parameters. Finally, the technique for order preference by similarity to ideal solution is employed to select the optimal combination of operational parameters for geothermal extraction. It is concluded that water loss and thermal drawdown are necessarily considered in the optimization process. The proposed approach represents global optimization. Operational parameters for the optimal case are (Qin, Tin, pout) which equal (49.98 °C, 62.21 kg/s, 27.44 MPa) under the conditions of this study. From the comparison between the base case and optimal case, it is observed that horizontal spread length, 2D swept area and 3D swept volume of the low-temperature scope is reduced by 77.9 m, 10 × 104 m2, and 16 × 106 m3. Besides, the thermal breakthrough has been delayed by 1.1 years. The water loss decreases by 36.23%. The optimal case demonstrates a great improvement in production performance. Sustainable exploitation is achieved through multi-objective optimization for operational parameters. The proposed method reflects superiority, efficiency, and intelligence in geothermal development.@en

Operational parameters optimization is of great significance to improve overall heat extraction performance from the hydrothermal or enhanced geothermal system. Injection flowrate, injection temperature, and production pressure are several of the easily human-controlled parameters to make the best of limited geothermal resources during the planned life. The net heat power and flow impedance are two contradictory production indexes to be optimized for efficient exploitation of long-term geothermal production. In this study, an integrated approach of finite element, multiple regression, non-dominated sorting genetic algorithm, and the technique for order preference by similarity to ideal solution is proposed and applied to the multilateral-well system to realize the optimization of geothermal development. Firstly, parametric cases coupling with thermal and hydraulic models are analyzed. Then, multiple regression is employed to obtain the net heat power and flow impedance functions considering human-controlled operational parameters and reservoir physical properties. Afterward, the multi-objective optimization algorithm is used to gain the Pareto solution set of injection and production parameters. Finally, the technique for order preference by similarity to ideal solution is employed to select the optimal combination of operational parameters for geothermal extraction. It is concluded that water loss and thermal drawdown are necessarily considered in the optimization process. The proposed approach represents global optimization. Operational parameters for the optimal case are (Qin, Tin, pout) which equal (49.98 °C, 62.21 kg/s, 27.44 MPa) under the conditions of this study. From the comparison between the base case and optimal case, it is observed that horizontal spread length, 2D swept area and 3D swept volume of the low-temperature scope is reduced by 77.9 m, 10 × 104 m2, and 16 × 106 m3. Besides, the thermal breakthrough has been delayed by 1.1 years. The water loss decreases by 36.23%. The optimal case demonstrates a great improvement in production performance. Sustainable exploitation is achieved through multi-objective optimization for operational parameters. The proposed method reflects superiority, efficiency, and intelligence in geothermal development.@en

Operational parameters optimization is of great significance to improve overall heat extraction performance from the hydrothermal or enhanced geothermal system. Injection flowrate, injection temperature, and production pressure are several of the easily human-controlled parameters to make the best of limited geothermal resources during the planned life. The net heat power and flow impedance are two contradictory production indexes to be optimized for efficient exploitation of long-term geothermal production. In this study, an integrated approach of finite element, multiple regression, non-dominated sorting genetic algorithm, and the technique for order preference by similarity to ideal solution is proposed and applied to the multilateral-well system to realize the optimization of geothermal development. Firstly, parametric cases coupling with thermal and hydraulic models are analyzed. Then, multiple regression is employed to obtain the net heat power and flow impedance functions considering human-controlled operational parameters and reservoir physical properties. Afterward, the multi-objective optimization algorithm is used to gain the Pareto solution set of injection and production parameters. Finally, the technique for order preference by similarity to ideal solution is employed to select the optimal combination of operational parameters for geothermal extraction. It is concluded that water loss and thermal drawdown are necessarily considered in the optimization process. The proposed approach represents global optimization. Operational parameters for the optimal case are (Qin, Tin, pout) which equal (49.98 °C, 62.21 kg/s, 27.44 MPa) under the conditions of this study. From the comparison between the base case and optimal case, it is observed that horizontal spread length, 2D swept area and 3D swept volume of the low-temperature scope is reduced by 77.9 m, 10 × 104 m2, and 16 × 106 m3. Besides, the thermal breakthrough has been delayed by 1.1 years. The water loss decreases by 36.23%. The optimal case demonstrates a great improvement in production performance. Sustainable exploitation is achieved through multi-objective optimization for operational parameters. The proposed method reflects superiority, efficiency, and intelligence in geothermal development.@en

Operational parameters optimization is of great significance to improve overall heat extraction performance from the hydrothermal or enhanced geothermal system. Injection flowrate, injection temperature, and production pressure are several of the easily human-controlled parameters to make the best of limited geothermal resources during the planned life. The net heat power and flow impedance are two contradictory production indexes to be optimized for efficient exploitation of long-term geothermal production. In this study, an integrated approach of finite element, multiple regression, non-dominated sorting genetic algorithm, and the technique for order preference by similarity to ideal solution is proposed and applied to the multilateral-well system to realize the optimization of geothermal development. Firstly, parametric cases coupling with thermal and hydraulic models are analyzed. Then, multiple regression is employed to obtain the net heat power and flow impedance functions considering human-controlled operational parameters and reservoir physical properties. Afterward, the multi-objective optimization algorithm is used to gain the Pareto solution set of injection and production parameters. Finally, the technique for order preference by similarity to ideal solution is employed to select the optimal combination of operational parameters for geothermal extraction. It is concluded that water loss and thermal drawdown are necessarily considered in the optimization process. The proposed approach represents global optimization. Operational parameters for the optimal case are (Qin, Tin, pout) which equal (49.98 °C, 62.21 kg/s, 27.44 MPa) under the conditions of this study. From the comparison between the base case and optimal case, it is observed that horizontal spread length, 2D swept area and 3D swept volume of the low-temperature scope is reduced by 77.9 m, 10 × 104 m2, and 16 × 106 m3. Besides, the thermal breakthrough has been delayed by 1.1 years. The water loss decreases by 36.23%. The optimal case demonstrates a great improvement in production performance. Sustainable exploitation is achieved through multi-objective optimization for operational parameters. The proposed method reflects superiority, efficiency, and intelligence in geothermal development.@en

Operational parameters optimization is of great significance to improve overall heat extraction performance from the hydrothermal or enhanced geothermal system. Injection flowrate, injection temperature, and production pressure are several of the easily human-controlled parameters to make the best of limited geothermal resources during the planned life. The net heat power and flow impedance are two contradictory production indexes to be optimized for efficient exploitation of long-term geothermal production. In this study, an integrated approach of finite element, multiple regression, non-dominated sorting genetic algorithm, and the technique for order preference by similarity to ideal solution is proposed and applied to the multilateral-well system to realize the optimization of geothermal development. Firstly, parametric cases coupling with thermal and hydraulic models are analyzed. Then, multiple regression is employed to obtain the net heat power and flow impedance functions considering human-controlled operational parameters and reservoir physical properties. Afterward, the multi-objective optimization algorithm is used to gain the Pareto solution set of injection and production parameters. Finally, the technique for order preference by similarity to ideal solution is employed to select the optimal combination of operational parameters for geothermal extraction. It is concluded that water loss and thermal drawdown are necessarily considered in the optimization process. The proposed approach represents global optimization. Operational parameters for the optimal case are (Qin, Tin, pout) which equal (49.98 °C, 62.21 kg/s, 27.44 MPa) under the conditions of this study. From the comparison between the base case and optimal case, it is observed that horizontal spread length, 2D swept area and 3D swept volume of the low-temperature scope is reduced by 77.9 m, 10 × 104 m2, and 16 × 106 m3. Besides, the thermal breakthrough has been delayed by 1.1 years. The water loss decreases by 36.23%. The optimal case demonstrates a great improvement in production performance. Sustainable exploitation is achieved through multi-objective optimization for operational parameters. The proposed method reflects superiority, efficiency, and intelligence in geothermal development.@en

Operational parameters optimization is of great significance to improve overall heat extraction performance from the hydrothermal or enhanced geothermal system. Injection flowrate, injection temperature, and production pressure are several of the easily human-controlled parameters to make the best of limited geothermal resources during the planned life. The net heat power and flow impedance are two contradictory production indexes to be optimized for efficient exploitation of long-term geothermal production. In this study, an integrated approach of finite element, multiple regression, non-dominated sorting genetic algorithm, and the technique for order preference by similarity to ideal solution is proposed and applied to the multilateral-well system to realize the optimization of geothermal development. Firstly, parametric cases coupling with thermal and hydraulic models are analyzed. Then, multiple regression is employed to obtain the net heat power and flow impedance functions considering human-controlled operational parameters and reservoir physical properties. Afterward, the multi-objective optimization algorithm is used to gain the Pareto solution set of injection and production parameters. Finally, the technique for order preference by similarity to ideal solution is employed to select the optimal combination of operational parameters for geothermal extraction. It is concluded that water loss and thermal drawdown are necessarily considered in the optimization process. The proposed approach represents global optimization. Operational parameters for the optimal case are (Qin, Tin, pout) which equal (49.98 °C, 62.21 kg/s, 27.44 MPa) under the conditions of this study. From the comparison between the base case and optimal case, it is observed that horizontal spread length, 2D swept area and 3D swept volume of the low-temperature scope is reduced by 77.9 m, 10 × 104 m2, and 16 × 106 m3. Besides, the thermal breakthrough has been delayed by 1.1 years. The water loss decreases by 36.23%. The optimal case demonstrates a great improvement in production performance. Sustainable exploitation is achieved through multi-objective optimization for operational parameters. The proposed method reflects superiority, efficiency, and intelligence in geothermal development.@en

Geothermal reservoir scaling controlled by reactive flow is a common phenomenon during geothermal development, which might lead to the decline of geothermal productivity and abandonment of geothermal projects. It is significant to reveal chemical influence on the geothermal reservoir characteristics and production performance to provide the reference for geothermal sustainable development. In this work, a thermal-hydraulic-chemical coupling model is developed for a triple-well fractured enhanced geothermal system considering the silica dissolution and precipitation at the fracture surface. It is focused on the evolution of fracture aperture and geothermal productivity. The results indicate that undersaturated injection will cause mineral dissolution which leads to the rise of fracture transmissibility and a thermal break. The fracture width is enhanced by 0.093 mm, and the permeability is 8 times the initial value after 20-year production. The oversaturated injection will cause mineral precipitation which leads to the reduction of fracture aperture and the increase of pressure difference. The opening is reduced by 0.041 mm and the permeability goes down by 2 orders of magnitude. The maximum increases of average production temperature and differential pressure are 10 °C and 13.5 MPa respectively in the 20th year from undersaturation to supersaturation. Besides, it is figured out that reaction rate denotes the evolution of fracture aperture. Both of them are distributed as a band at a fracture plane, which is determined by saturation index and temperature together. The former controls the reaction pattern (dissolution/precipitation) while the latter controls the reaction speed. Furthermore, the evolution of opening is uneven around the production well at different exploitation stages. Fracture width is higher on the strong side in the first 8.7 years but is higher on the weak side in later development when undersaturated water (0 mol/kg) is injected. The phenomenon can be employed to judge the production status, guide the descaling, etc. Production analysis indicates that oversaturation delays the heat breakthrough and undersaturation reduces the surface pump input. This study will provide a significant reference for the geothermal sustainable production influenced by chemical reactions in an enhanced geothermal system.@en

Geothermal reservoir scaling controlled by reactive flow is a common phenomenon during geothermal development, which might lead to the decline of geothermal productivity and abandonment of geothermal projects. It is significant to reveal chemical influence on the geothermal reservoir characteristics and production performance to provide the reference for geothermal sustainable development. In this work, a thermal-hydraulic-chemical coupling model is developed for a triple-well fractured enhanced geothermal system considering the silica dissolution and precipitation at the fracture surface. It is focused on the evolution of fracture aperture and geothermal productivity. The results indicate that undersaturated injection will cause mineral dissolution which leads to the rise of fracture transmissibility and a thermal break. The fracture width is enhanced by 0.093 mm, and the permeability is 8 times the initial value after 20-year production. The oversaturated injection will cause mineral precipitation which leads to the reduction of fracture aperture and the increase of pressure difference. The opening is reduced by 0.041 mm and the permeability goes down by 2 orders of magnitude. The maximum increases of average production temperature and differential pressure are 10 °C and 13.5 MPa respectively in the 20th year from undersaturation to supersaturation. Besides, it is figured out that reaction rate denotes the evolution of fracture aperture. Both of them are distributed as a band at a fracture plane, which is determined by saturation index and temperature together. The former controls the reaction pattern (dissolution/precipitation) while the latter controls the reaction speed. Furthermore, the evolution of opening is uneven around the production well at different exploitation stages. Fracture width is higher on the strong side in the first 8.7 years but is higher on the weak side in later development when undersaturated water (0 mol/kg) is injected. The phenomenon can be employed to judge the production status, guide the descaling, etc. Production analysis indicates that oversaturation delays the heat breakthrough and undersaturation reduces the surface pump input. This study will provide a significant reference for the geothermal sustainable production influenced by chemical reactions in an enhanced geothermal system.@en