Geothermal Energy最新文献

Characterizing the evolution of mechanical properties of hot dry rock (HDR) after supercritical CO₂ (CO₂(sc)) injection is crucial for assessing the heat extraction rate and reservoir security of CO₂ based enhanced geothermal systems. This study designed the experiments of triaxial seepage and mechanical properties considering no CO₂(sc) injection, CO₂(sc) injection, and alternating injection of water-CO₂(sc) (AIWC) in granite at 150–300 ℃. The experiments can reveal the mechanical properties of HDR in single-phase CO₂ zone, CO₂-water two-phase zone and dissolved CO₂ liquid phase zone in HDR reservoir. The results indicate that the failure mode of the rock samples primarily exhibits sudden instability after no CO₂(sc) injection and AIWC, whereas it predominantly manifests progressive instability after CO₂(sc) injection. Compared with 25 ℃, the uniaxial compressive strength (UCS) after no CO₂(sc) injection at 150–300 ℃ decreased by 13.86%–32.92%. After CO₂(sc) injection, the UCS decreased by 40.79%–59.60%. After AIWC, the UCS decreased by 27.74–40.48%. This shows that the strength of rock mass in the single-phase CO₂ zone is lower than that in the other two zones, and this weakening phenomenon increases with the increase of temperature difference. At the same temperature, the elasticity modulus after AIWC was greater than that after no CO₂(sc) injection and CO₂(sc) injection. With no CO₂(sc) injection, when the temperature was increased to 200 ℃ and 300 ℃, intergranular cracks and transgranular appeared respectively. After AIWC, mineral crystals such as calcite were precipitated on the surfaces of the connected large cracks, accompanied by kaolinite clay minerals. This increases the frictional contact of the mineral particles and enhances the stability of the HDR reservoir.

The development of a novel method for modelling fluid flow and heat transfer in naturally fractured geothermal reservoirs represents a significant advancement in geothermal energy research. This Study presents a hybrid approach, which combines discrete fracture and single continuum techniques, to effectively capture the complex interactions between fluid flow and heat transfer in geothermal fractured reservoirs. In addition, the incorporation of the local thermal nonequilibrium method for simulating heat transmission accounts for the disparities in temperature between the rock matrix and the fluid, providing a more realistic representation of heat transfer processes. The study also presents a fully coupled thermo-poro-elastic framework that integrates fluid flow and heat transfer to comprehensively evaluate reservoir responses to injection/production scenarios. This coupled approach allows for the prediction of changes in reservoir properties, such as permeability and porosity, under varying fluid pressure and temperature conditions. The application of the proposed model to evaluate a geothermal reservoir’s long-term response to injection/production scenarios provides valuable insights into the reservoir’s behaviour and potential energy production capacity. The sensitivity analysis further enhances the model’s utility by identifying the key reservoir parameters that significantly influence the thermal depletion of the reservoir. Overall, this novel modelling approach holds promise for improving the understanding and management of naturally fractured geothermal reservoirs, contributing to the optimization of geothermal energy extraction strategies.

This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop a thermal Earth model for the conterminous United States. The model was trained to approximately satisfy Fourier’s Law of conductive heat transfer by simultaneously predicting subsurface temperature, surface heat flow, and rock thermal conductivity. In addition to bottomhole temperature measurements, we incorporated other spatial and physical quantities as model inputs, such as depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, electrical conductivity, and proximity to faults and volcanoes. With a spatial resolution of (18 km^2) per grid cell, we predicted heat flow at surface as well as temperature and rock thermal conductivity across depths of (0-7 km) at an interval of (1 km). Our model showed temperature, surface heat flow and thermal conductivity mean absolute errors of (6.4^circ C), (6.9 mW/m^2) and (0.04 W/m-K), respectively. This thorough modeling of the Earth’s thermal processes is crucial to understanding subsurface phenomena and exploiting natural underground resources. Our thermal Earth model is available as web application at https://stm.stanford.edu, feature layers on ArcGIS at https://arcg.is/nLzzT0, and tabulated data on the Geothermal Data Repository at https://gdr.openei.org/submissions/1592.

Model predictive control (MPC) is a promising approach for optimizing the performance of borehole heat exchangers (BHEs) in ground-source heat pump systems. The central element of MPC is the forward model that predicts the thermal dynamics in the ground. In this work, we validate the prediction accuracy of four BHE modeling approaches against real-world measurement data across various operational events and timescales. We simulate the fluid temperature leaving a BHE using a fully discretized 3-D numerical model, a resistance–capacitance model, a g-function model, and a hybrid model. The simulated temperatures are compared to measured temperatures using three validation metrics that quantify temperature offset, noise, and accuracy. The main reason for a mismatch between measured and modeled temperatures is a temperature offset of the simulated temperature. To remove this effect, the models were calibrated for their most sensitive parameter, the ground temperature, and their prediction accuracy over 4 years was evaluated. Thereby, model calibration seems to be a viable solution to account for an unknown load history. The results show that the resistance–capacitance model provides decent predictions in the short term and the g-function model in the long term. However, both models are strongly dependent on accurate calibration. The hybrid model provides the most accurate short and long-term predictions and is less dependent on calibration. Still, its integration into optimization syntax poses challenges compared to the other models. Although not yet applied in model predictive control, the hybrid model stands out as a promising choice for optimizing BHE field operations across various timescales.

Space heating applications account for a high share of global greenhouse gas emissions. To increase the renewable share of heat generation, seasonal thermal energy storage (STES) can be used to make thermal energy from fluctuating renewable sources available in times of high demand. A popular STES technology is pit thermal energy storage (PTES), where heat is stored underground, using water as a storage medium. To evaluate the use of PTES in an energy system, easily adaptable, publicly accessible and tool independent models are needed. In this paper, we improve an existing PTES model developed in the Modelica modeling language. The model is cross-compared with a more detailed and previously validated COMSOL model, considering different amounts of insulation, showing a deviation of 2–13% in the observed annual charged and discharged amount of heat. The results indicate that the presented model is well suited for early design stage and an exemplary case study is performed to demonstrate its applicability in a system context. Dimensions of system components are optimized for the levelized cost of heat (LCOH), both with and without subsidies, highlighting the importance of subsidies for the transition towards climate friendly heating solutions, as the gas boiler use is reduced from 47.6% to 2.7%.

Hot dry rock (HDR) resources are gaining increasing attention as a significant renewable resource due to their low carbon footprint and stable nature. When assessing the potential of a conventional geothermal resource, a temperature field distribution is a crucial factor. However, the available geostatistical and numerical simulations methods are often influenced by data coverage and human factors. In this study, the Convolution Block Attention Module (CBAM) and Bottleneck Architecture were integrated into UNet (CBAM-B-UNet) for simulating the geothermal temperature field. The proposed CBAM-B-UNet takes in a geological model containing parameters such as density, thermal conductivity, and specific heat capacity as input, and it simulates the temperature field by dynamically blending these multiple parameters through the neural network. The bottleneck architectures and CBAM can reduce the computational cost while ensuring accuracy in the simulation. The CBAM-B-UNet was trained using thousands of geological models with various real structures and their corresponding temperature fields. The method’s applicability was verified by employing a complex geological model of hot dry rock. In the final analysis, the simulated temperature field results are compared with the theoretical steady-state crustal ground temperature model of Gonghe Basin. The results indicated a small error between them, further validating the method's superiority. During the temperature field simulation, the thermal evolution law of a symmetrical cooling front formed by low thermal conductivity and high specific heat capacity in the center of the fault zone and on both sides of granite was revealed. The temperature gradually decreases from the center towards the edges.

Stimulation techniques to enhance fluid pathways are an important tool to make geothermal projects economically feasible. So far, hydraulic stimulation is used almost exclusively for reservoir-wide improvement of the permeability, but induced seismicity poses a challenge. Chemical stimulation on the other hand has been limited to the close vicinity of the borehole and has barely been considered for the creation of enhanced geothermal reservoirs. However, retardation mechanisms reducing the chemical reaction rate can be used to increase the radius of the chemical stimulation thus enabling a reservoir-wide enhancement of fluid pathways. In this work, we review the technologies of retardation mechanisms for chemical stimulation in geothermal systems and identify five groups of retardation techniques: (i) causing impaired mobility of the acid, e.g., by gelling agents; (ii) causing an impaired dissociation, e.g., by the in-situ generation of the reactive compounds; (iii) blocking the mineral surface area, e.g., by alternating injections of pad fluids and acids; (iv) reducing the reaction rate constant, e.g., by cooling; and (v) changing the chemical equilibrium through chelating agents. We found that most applications are currently based on the use of impaired dissociation, but present research focuses on the development and application of chelating agents. Most of these retardation techniques are adopted from the hydrocarbon industry, but there are several techniques that have not been applied in the geothermal context so far for various reasons. We identify a distinctive lack of in-depth descriptions of the retardation techniques in various studies—mostly to protect intellectual property. However, in the light of public concern regarding fracking techniques and to independently assess potential environmental hazards, scientific examination of proposed techniques is indispensable.

Deep borehole heat exchangers (DBHEs) with depths exceeding 500 m have been researched comprehensively in the literature, focusing on both applications and subsurface modelling. This review focuses on conventional (vertical) DBHEs and provides a critical literature survey to analyse (i) methodologies for modelling; (ii) results from heat extraction modelling; (iii) results from modelling deep borehole thermal energy storage; (iv) results from heating and cooling models; and (v) real case studies. Numerical models generally compare well to analytical models whilst maintaining more flexibility, but often with increased computational resources. Whilst in-situ geological parameters cannot be readily modified without resorting to well stimulation techniques (e.g. hydraulic or chemical stimulation), engineering system parameters (such as mass flow rate of the heat transfer fluid) can be optimised to increase thermal yield and overall system performance, and minimise pressure drops. In this active research area, gaps remain, such as limited detailed studies into the effects of geological heterogeneity on heat extraction. Other less studied areas include: DBHE arrays, boundary conditions and modes of operation. A small number of studies have been conducted to investigate the potential for deep borehole thermal energy storage (BTES) and an overview of storage efficiency metrics is provided herein to bring consistency to the reporting of thermal energy storage performance of such systems. The modifications required to accommodate cooling loads are also presented. Finally, the active field of DBHE research is generating a growing number of case studies, particularly in areas with low-cost drilling supply chains or abandoned hydrocarbon or geothermal wells suitable for repurposing. Existing and planned projects are thus presented for conventional (vertical) DBHEs. Despite growing interest in this area of research, further work is needed to explore DBHE systems for cooling and thermal energy storage.