Hot dry rocks (HDRs), as an essential renewable energy source, its development has received widespread attention, especially for heat extraction. The fracture is the main seepage and heat transfer channel of circulating fluid in dense HDR reservoirs, and its conductivity evolution significantly affects the production performance. Most existing studies have focused on the change of fracture conductivity under elastic deformation without considering the additional conductivity induced by rock damage. However, the additional conductivity may have significant implications for rational design and timely adjustment of the production scheme. Therefore, a three-dimensional model at the field-scale is established, and it is used to analyze the effect of additional conductivity on production performance and economic efficiency. To simplify the calculation, the actual forms of damage are equivalent to the macroscopic physical evolution of the matrix. Results show that the rock is mainly tensile failure affected by thermal stress during production. The occurrence of damage will increase the reservoir permeability and porosity, reduce Young's modulus, and then reduce the differential pressure and production temperature, with a maximum reduction of 2.21 MPa and 14.21 °C in the control case, respectively. The effects of injection temperature, Young's modulus, and injection mass flow on the production performance are significant, followed by Poisson's ratio. In contrast, production pressure and fracture initial permeability had less influence. The maximum differential economic benefit of the control case is up to 2.289 million RMB. This research proves the necessity of damage study during the long-term production of HDRs.@en

Fractures and caves are the main flow and storage channels for the karst geothermal reservoirs, and the water-rock reaction within them significantly affects the thermal performance. Most previous studies concentrated on the fractures, disregarding the impact of the pore water-rock reaction. The objective of this study is to explore the importance of pore water-rock reactions and identify the influence of various parameters when considering pore and fracture water-rock reactions. A 3D thermal-hydraulic-chemical coupling model considering dual media of pores and fractures was developed. The importance of pore water-rock reactions is demonstrated, and quantitatively characterize the effect of injection temperature (Tin), injection rate (Qin), injection concentration (cin), and ratio of the reaction-specific surface area between pore and fracture (Ap/Af) on the thermal performance. Results indicate that the pore water-rock reaction drastically affects the hydraulic conductivity and pressure difference, even leading to an opposite trend. The influence of water-rock reaction in pores on fracture deformation is regulated by Ap/Af, which augments with Ap/Af. The relative contribution of Ap/Af to production temperature, net thermal power, pressure difference, and hydraulic conductivity are 12.8%, 4.1%, 6.8%, and 13.7%, respectively. This study provides a significant guide for accurate production prediction and exploitation of karst-based geothermal reservoirs.@en

The development of hot dry rocks (HDRs) is of great significance to adjusting energy structure, alleviating energy shortage, reducing pollution, etc. Low-permeability granite is the predominant rock type in deep HDRs, making fractures the primary pathways for fluid circulation and heat extraction. The production of HDRs is significantly influenced by variable fracture conductivity, but current conductivity characterization primarily relies on the elastic deformation of the matrix, neglecting the impact of damage. Accordingly, we propose an experimental method and a supporting apparatus, which is used to unveil the conductivity evolution characteristics resulting from the comprehensive effects of damage and elastic deformation. The experimental results demonstrate that when subjected to confining force squeezing inward, the fracture conductivity experiences varying degrees of decrease compared to its initial state before the experiment. By utilizing the conductivity evolution rate as the evaluation criterion and conducting grey correlation analysis, it has been determined that temperature exerts the most significant influence on the conductivity evolution, followed by injection flow, and lastly, confining pressure. Moreover, rock particle types and production cycles also have different degrees of effect. After considering the comprehensive effects of damage-elastic deformation at the field-scale, the damage has a positive effect on conductivity enhancement. Our study provides a new approach for the characterization of fracture conductivity evolution for deep geothermal projects.@en

The development of hot dry rocks (HDRs) is of great significance to adjusting energy structure, alleviating energy shortage, reducing pollution, etc. Low-permeability granite is the predominant rock type in deep HDRs, making fractures the primary pathways for fluid circulation and heat extraction. The production of HDRs is significantly influenced by variable fracture conductivity, but current conductivity characterization primarily relies on the elastic deformation of the matrix, neglecting the impact of damage. Accordingly, we propose an experimental method and a supporting apparatus, which is used to unveil the conductivity evolution characteristics resulting from the comprehensive effects of damage and elastic deformation. The experimental results demonstrate that when subjected to confining force squeezing inward, the fracture conductivity experiences varying degrees of decrease compared to its initial state before the experiment. By utilizing the conductivity evolution rate as the evaluation criterion and conducting grey correlation analysis, it has been determined that temperature exerts the most significant influence on the conductivity evolution, followed by injection flow, and lastly, confining pressure. Moreover, rock particle types and production cycles also have different degrees of effect. After considering the comprehensive effects of damage-elastic deformation at the field-scale, the damage has a positive effect on conductivity enhancement. Our study provides a new approach for the characterization of fracture conductivity evolution for deep geothermal projects.@en

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

In the long-term mining of geothermal resources in hot dry rock (HDR), the change of thermal stress and pore pressure will increase fracture conductivity evolution, further improving production performance. The optimization and decision-making of the development scheme based on the impact of damage from fractures have yet to be reported. The damage to fractures is essential in designing and adjusting geothermal resource development schemes, particularly in selecting optimal schemes. Therefore, the production performances of HDR resources under different parameters are analyzed to establish a database. Then, minimizing flow resistance, maximizing net power, and maximizing economic benefits are set as optimization goals. Various injection-mining parameters and fracture characteristics are treated as decision variables. Multi-objective optimization and multi-attribute decision analysis is conducted to obtain optimal schemes. Finally, optimal schemes are evaluated and compared, considering damage and non-damage scenarios. Results show that the NSGA-II algorithm is more suitable for optimizing geothermal development questions. Net power and economic benefits of the optimal scheme considering damage increase by 45.84 % and 21.35 % compared to the control scheme with damage. For the non-damage scenario, the above values increased by 31.55 % and 5.15 %, respectively. Compared to not considering the damage, higher mass flow and well spacing of optimal scheme can be selected for the case when damaged. Moreover, the parametric design of the optimal scheme becomes more conservative as the production cycle increases.@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

Geothermal energy is one of renewable and clean energy resources. Predicting geothermal productivity is an essential task for managing a continuable geothermal system, which is a huge challenge due to the highly non-linear relationship between the productivity and constraint conditions, such as reservoir properties and operational conditions. Using numerical simulation to predict the geothermal productivity is computationally expensive and very time consuming. Therefore, this study proposes a novel Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) combinational neural network to effectively forecast the geothermal productivity considering constraint conditions. In the LSTM and MLP combinational neural network, MLP is trained to learn the non-linear relationship between the geothermal productivity and constraint conditions, while LSTM is used to memorize sequential relations within the production data. We comprehensively evaluate the geothermal productivity prediction performance of the LSTM and MLP combinational network. It indicates that the LSTM and MLP combinational neural network could accurately and stably predict the geothermal productivity and has a good generalization ability. Compared with original LSTM, MLP, Simple Recurrent Neural Network (RNN), the LSTM and MLP combinational network demonstrates the best geothermal productivity prediction accuracy, stability and generalization ability. This study provides a high precision and efficiency forecasting method for the geothermal productivity prediction.@en

Geothermal energy is one of renewable and clean energy resources. Predicting geothermal productivity is an essential task for managing a continuable geothermal system, which is a huge challenge due to the highly non-linear relationship between the productivity and constraint conditions, such as reservoir properties and operational conditions. Using numerical simulation to predict the geothermal productivity is computationally expensive and very time consuming. Therefore, this study proposes a novel Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) combinational neural network to effectively forecast the geothermal productivity considering constraint conditions. In the LSTM and MLP combinational neural network, MLP is trained to learn the non-linear relationship between the geothermal productivity and constraint conditions, while LSTM is used to memorize sequential relations within the production data. We comprehensively evaluate the geothermal productivity prediction performance of the LSTM and MLP combinational network. It indicates that the LSTM and MLP combinational neural network could accurately and stably predict the geothermal productivity and has a good generalization ability. Compared with original LSTM, MLP, Simple Recurrent Neural Network (RNN), the LSTM and MLP combinational network demonstrates the best geothermal productivity prediction accuracy, stability and generalization ability. This study provides a high precision and efficiency forecasting method for the geothermal productivity prediction.@en

Geothermal energy is one of renewable and clean energy resources. Predicting geothermal productivity is an essential task for managing a continuable geothermal system, which is a huge challenge due to the highly non-linear relationship between the productivity and constraint conditions, such as reservoir properties and operational conditions. Using numerical simulation to predict the geothermal productivity is computationally expensive and very time consuming. Therefore, this study proposes a novel Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) combinational neural network to effectively forecast the geothermal productivity considering constraint conditions. In the LSTM and MLP combinational neural network, MLP is trained to learn the non-linear relationship between the geothermal productivity and constraint conditions, while LSTM is used to memorize sequential relations within the production data. We comprehensively evaluate the geothermal productivity prediction performance of the LSTM and MLP combinational network. It indicates that the LSTM and MLP combinational neural network could accurately and stably predict the geothermal productivity and has a good generalization ability. Compared with original LSTM, MLP, Simple Recurrent Neural Network (RNN), the LSTM and MLP combinational network demonstrates the best geothermal productivity prediction accuracy, stability and generalization ability. This study provides a high precision and efficiency forecasting method for the geothermal productivity prediction.@en

Salt cavern is one of the significant types of underground gas storage. During the construction of the salt cavern gas storage, part of the insoluble particles will remain in the cavity. The insoluble particles gradually deposit in the bottom of the salt cave, which reduces the cavity construction speed and lessens the volume of gas storage. The high-pressure rotating water jet technology is simulated to flush insoluble particles, and the flushing system is mainly composed of surface facilities and downhole flushing tools. The seven high-pressure rotating water jets can flush the insoluble particles at the bottom of the cavity. A mathematical model with the solid-liquid two-phase flow is established and numerical simulated first. Then, this paper analyzes the influence of the injection flowrate, the distance from inlet to outlet, the rotational velocity on the flushing efficiency. The results show that the injection flowrate and the distance from inlet to outlet are the two main controlling factors. Under the conditions of this paper, the injection flowrate is 100 m3/h, the rotational velocity is 300 r/min~400 r/min, the distance from inlet to outlet is between 0.5 m~1.0 m, and the flushing efficiency is higher than that of other range studied. The research is helpful to understand the flushing rule and optimize the flushing parameters during the construction of the gas storage through water jetting technology.@en

Salt cavern is one of the significant types of underground gas storage. During the construction of the salt cavern gas storage, part of the insoluble particles will remain in the cavity. The insoluble particles gradually deposit in the bottom of the salt cave, which reduces the cavity construction speed and lessens the volume of gas storage. The high-pressure rotating water jet technology is simulated to flush insoluble particles, and the flushing system is mainly composed of surface facilities and downhole flushing tools. The seven high-pressure rotating water jets can flush the insoluble particles at the bottom of the cavity. A mathematical model with the solid-liquid two-phase flow is established and numerical simulated first. Then, this paper analyzes the influence of the injection flowrate, the distance from inlet to outlet, the rotational velocity on the flushing efficiency. The results show that the injection flowrate and the distance from inlet to outlet are the two main controlling factors. Under the conditions of this paper, the injection flowrate is 100 m3/h, the rotational velocity is 300 r/min~400 r/min, the distance from inlet to outlet is between 0.5 m~1.0 m, and the flushing efficiency is higher than that of other range studied. The research is helpful to understand the flushing rule and optimize the flushing parameters during the construction of the gas storage through water jetting technology.@en