Geothermal Energy最新文献

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.

Efficient exploration of geothermal resources is the basis of exploitation and utilization of geothermal resources. In recent years, Geographic Information System (GIS) has been increasingly used for the exploration owing to its power ability to integrate and analyze multiple sources of data related to the formation of geothermal resources, such as geology, geophysics, and geochemistry. Correctly understanding the control effect of evidence factors on geothermal resources is the premise and basis of whether the prediction results of evidence weight model are accurate. Traditionally, the conventional weight of evidence model assume that each evidence factor exerts a uniform controlling effect on the formation and distribution of geothermal resources. However, recent research indicates significant variations in the controlling ability of factors such as faults and granites, influenced by factors like activity levels and crystalline ages. Yet, studies addressing this differential control are lacking. To address this gap, we propose a series of weight of evidence models using abundant geological, geophysical, and geothermal data from the western Sichuan plateau, a high-temperature geothermal hotspot in China. This study aims to investigate the impact of varying controlling abilities of evidence factors on the evaluation model, with faults and granites as a case. Performance metrics include prediction rate, success rate index, receiver operating characteristic curve (ROC) and prediction rate of geothermal well. The findings of this research reveal that the weight of evidence model developed through the methodology outlined in this study exhibits superior performance compared to the conventional weight of evidence model. This superiority is evidenced by higher prediction rates, success indices, prediction rate of geothermal wells, and larger AUC values of ROC. Among these models, the weight of evidence model considering both fault and granite classification have the best performance in model evaluation indicators, with a prediction rate of 22.528 and a success index of 0.015408 in the very high potential area. The prediction rate and success index of the high potential area are 3.656 and 0.0025, respectively, and the prediction rate and success index of the middle potential area are 1.649 and 0.001128, respectively, and the AUC value is 0.808, indicating that the model has good accuracy. In terms of geothermal well prediction, the total prediction rate of geothermal favorable areas based on fault and granite classification evidence weight model is as high as 47.0526. Therefore, when constructing the weight of evidence model, the influence of the difference control of evidence factors on the formation of geothermal resources should be fully considered. These results underscore the effectiveness of the proposed methodology in enhancing the predictive accuracy and reliability of geothermal resource assessment in this study. Based on the prediction