Geothermal Energy最新文献

Exergoeconomics is a vital complementation of thermodynamic performance analysis. In this study, a comprehensive exergoeconomic analysis of a binary geothermal power plant in southwestern Anatolia is conducted to determine improvement potentials in the plant configuration. By utilization of cost allocation rules of three different exergoeconomic methods (Moran, Specific Exergy Costing (SPECO), Exergy Cost Theory) plant is analyzed in terms of exergetic costs and possible optimization areas. Levelized cost of electricity (LCOE) estimated by 3 different methods vary within a 3.6% range, between 7.81 c$/kWh and 8.1 c$/kWh. It has also been determined that 51.5% of LCOE is constituted by waste/residual costs. Components especially including a thermal phase change or energy conversion, whose exergoeconomic factors below 0.5 warrant investment and optimization for performance improvement despite their higher individual exergetic efficiencies reported in previous studies. This phenomenon highlights the importance of considering exergetic efficiency and exergoeconomic factors together as plant design parameters. By using advanced materials or by optimizing the temperature gradient between the geothermal brine and the working fluid, heat transfer efficiency can be enhanced in heat exchanger devices. Turbines generally have mechanical losses which can be enhanced by optimizing blade design, reducing friction, and enhancing the thermodynamic cycle (i.e., use re-heat stages or improve steam conditions).

The development of geothermal energy through enhanced geothermal systems (EGS) often encounters challenges such as fluid short-circuiting, water loss, and insufficient connectivity. This study presents a time-dependent seepage and heat exchange model for the formation–wellbore–fluid system during the heat extraction process. Taking the Fenton Hill HDR project as a case study, this paper investigates the influence of formation characteristics, wellbore design, and injected fluid properties on heat transfer efficiency. Furthermore, a multi-well EGS utilizing multiple fracturing horizontal wells (MFHW) is proposed, and its production temperature is compared with two types of double-well EGS. The findings reveal that within the horizontal segment of the double-well EGS, an optimal output of 3.4 MW can be achieved at an injection rate of 30 kg/s. Additionally, the extraction temperature shows a positive correlation with factors such as heat production and electrical power generation. In the MFHW project, optimizing heat production potential can be accomplished by increasing the number of perforation fractures, enhancing artificial fracture spacing, improving the perforation angle, extending the horizontal segment, reducing well diameter, and employing a longer vertical heat insulation pipe with lower thermal conductivity. Finally, a comparative analysis of various development models indicates that two-injection-one-production multi-well EGS model exhibits superior performance, with its heat production being twice as efficient as that of one-injection-one-production double-well EGS model.

Hydraulic stimulation of pre-existing fractures and faults plays a significant role in improving hydraulic conductivity of the fracture network around injection and production wells in deep geothermal reservoirs. In present work, a three-dimensional distinct element method (3DEC, Itasca) is used to simulate the year 2000 hydraulic stimulation of GPK2 well of Soultz-sous-Forêts geothermal reservoir, where several major hydraulic stimulations have been performed and are well documented. The field scale numerical model of the reservoir (about 6000 × 4500 × 4500 m³) includes an explicit description of the main fault (FZ4770), was developed to constrain the large-scale hydromechanical properties of the fault, in particular, its behavior in terms of non-linear elastic response related to fault aperture changes. The first phase of the stimulation is modelled as a constant flow rate of 30 ls⁻¹ of water injection into the center of a deformable fault at the depth of approximately 4.7 km. We observed that the fluid pressure front migration from the injection point along the fracture follows, under the in-situ stress condition and the moderate injection pressure, a pseudo-diffusion behavior as power-law function of time with a 0.5 exponent (√t) when the injection flow rate is constant. It is demonstrated that the dynamic evolution of aperture opening due to fluid injection into the fracture is responsible for the pressure propagation behavior, owing to a hydraulic aperture change rather than a fluid pressure diffusion process. This numerically observed propagation process is compatible with a high fault effective diffusivity of 13 m²/s as that observed in the field. In case of a linear increase of the injection flow rate, the pseudo-diffusion process disappears leading to a time dependent power-law migration of the pressure front with exponent of 0.75. The pressure propagation is shown to be strongly influenced by the injection scheme.

Deep geothermal closed-loops have recently gained attention because of their advantages over classical geothermal applications (e.g., less dependence on the geology, no risk of induced seismicity) and technological advantages (e.g., in the drilling process, use of alternative to water fluids). This paper deals with the repurposing of an existing well in Mol, Belgium, by numerically evaluating the closed-loop concept. Two numerical tools are used to predict the evolution of the temperature and the produced energy over a period of 20 years considering the vertical coaxial well and the complete geological morphology. Full-scale simulations are initially carried out to estimate the maximum capacity of the well and to highlight the need to control the output of the well by adjusting the inlet conditions. Simulations are then performed either to deliver a constant power or to cover irregular thermal energy demands of two buildings by applying in both cases three process control operations. Through controlling the inlet temperature, the injected flow rate or successively both, the production of excess energy, resulting from the overdesign of the existing wellbore for the specific application, is limited. The simulations showed that continuous adjustments to the injection temperature and/or flow rate are needed to restrict the rapid drop in outlet temperature and consequent thermal depletion of the rocks, caused by the highly transient nature of the diffusive heat transfer from the rocks to the wellbore, as well as to supply a specific heat demand, constant or irregular, over the long term. In fact, the combination of both controls could be the ideal strategy for supplying the demand at the highest COP.

Geothermal installations often face operational challenges related to scaling which can lead to loss in production, downtime, and an increase in operational costs. To accurately assess and minimize the risks associated with scaling, it is crucial to understand the interplay between geothermal brine composition, operating conditions, and pipe materials. The accuracy of scaling predictive models can be impacted by uncertainties in the brine composition, stemming from sub-optimal sampling of geothermal fluid, inhibitor addition, or measurement imprecision. These uncertainties can be further increased for fluid at extreme conditions especially high salinity and temperature. This paper describes a comprehensive method to determine operational control strategies to minimize the scaling considering brine composition uncertainties. The proposed modelling framework to demonstrate the optimization under uncertainty workflow consists of a multiphase flow solver coupled with a geochemistry model and an uncertainty quantification workflow to locally estimate the probability of precipitation potential, including its impact on the hydraulic efficiency of the geothermal plant by increasing the roughness and/or decreasing the diameter of the casings and pipelines. For plant operation optimization, a robust control problem is formulated with scenarios which are generated based on uncertainties in brine composition using an exhaustive search method. The modelling and optimization workflow was demonstrated in a geothermal case study dealing with barite and celestite scaling in a heat exchanger. The results showed the additional insights in the potential impact of brine composition uncertainties (aleatoric uncertainties) in scaling potential and precipitation location. Comparing the outcome of optimization problem for the deterministic and fluid composition uncertainties, a change of up to 2.5% in the temperature control settings was observed to achieve the optimal coefficient of performance.

Geothermal energy is a large, renewable, and clean source of energy from the earth in the form of heat. Exploring the deeper layers of the Williston Basin has revealed favorable reservoir temperatures, particularly in the western areas where high heat flows are prevalent. The quality of a geothermal hotspot hinges on the reservoir quality index (RQI), which is determined by the accuracy of calculating the field reservoir permeability. The primary goal of this study is to apply machine learning techniques to accurately calculate the field permeability, which is important for optimizing the RQI. To enhance accuracy, we initially applied various clustering algorithms, including the density-based spatial clustering of applications with noise (DBSCAN), K-means, K-median, and hierarchical clustering methods, to delineate hydraulic flow units (HFU) within the reservoir using porosity, permeability and water saturation core data. Subsequently, regression models including supervised ML regression methods such as neural networks, support vector machine (SVM) regression, Gaussian process regression (GPR), ensemble regression, linear regression, and decision trees were employed for each flow unit to establish correlations and calculate field permeability with each of these models validated using cross-validation. In comparison to the other clustering methods, the hierarchical clustering method showed the best performance by showing a strong correlation between the actual and predicted permeability values. Overall, the SVM and GPR regression methods were observed to show consistent results with the training and testing datasets, with the SVM regression technique yielding higher R-squared values through regression across the different clustering techniques. In addition, cross-plots were employed to successfully delineate the Red River formation into distinct regions, aiding in the definition of formation lithology and the estimation of field water saturation. Our study showcases an integrated approach to predicting reservoir permeability, considering limited core data. ML emerges as an effective tool for characterizing the Red River formation as a geothermal hotspot in North Dakota, showcasing the potential for sustainable energy exploration and utilization which reduces the reliance on extensive coring in order to enhance geothermal exploration accuracy.

The comprehension of geothermal systems involves the efficient integration of geological, geophysical and geochemical tools that are crucial in unraveling the distinct features inherent in geothermal reservoirs. We provide a first approach to comprehending the geologically complex geothermal system in the Aachen area, which has been known for its natural thermal spring occurrences since Roman times. Through a comprehensive analysis involving geochemical interpretation of water samples, a review of 2D seismic profiles, stress analysis, and surface geology, a dynamic model has been built, which serves as a conceptual framework providing a clearer understanding of the system. The model characterizes a non-magmatic, detachment fault-controlled convective thermal system, wherein the reservoir exhibits mixed properties of the mainly Devonian carbonate rocks. NW–SE directed fault lines play a pivotal role in fluid transport, enabling the ascent of thermal waters without the need for additional energy. We additionally conducted magnetotelluric (MT) surveys and analyzed apparent resistivity and impedance values obtained through forward modeling, along with an assessment of noise levels. These findings contribute to evaluating the potential use of MT methods in further evaluating the study area and for geothermal energy exploration in general.

Crustal fault zones, holding promise as potential geothermal reservoirs, remain largely untapped and unexplored. Located in the southern Massif Central, France, the Margeride fault zone (MFZ) varies in thickness (lateral extension perpendicular to the fault plane) from 100 m to over 2500 m. Reactivated several times under different stress regimes since the Variscan orogeny, this zone is characterized by an intense alteration and fracturing. As a result, the multiple reactivation of the fault zone has maintained permeability, leading to favourable conditions for fluid circulation. Structural measurements and geological cross sections were used to precisely constrain thickness and geometry of the fault zone. North of the MFZ, the Coren thermal spring indicates reservoir temperatures of about 200–250 °C, hinting at the possible existence of a temperature anomaly. To investigate this geothermal potential, 3D numerical models simulating fluid circulation within a fault zone were conducted. Various configurations were explored, altering fault zone thickness and permeability for two key geometries. The first geometry, which manipulated the width of the fault zone along its length, demonstrated a direct correlation between fault zone thickness and amplitude of thermal anomaly. Thinner faults (< 500 m) exhibited multiple weak positive thermal anomalies, while thicker faults (> 500 m) tended to develop a single, substantial positive thermal anomaly. In the second examined geometry, where fault zone thickness increased longitudinally, a consistent positive temperature anomaly emerged at the thickest section of the fault zone. Depending on the permeability value, an additional anomaly may develop but will migrate laterally towards the thinnest part of the fault zone. This multi-disciplinary approach, combining numerical modelling and field measurements, presents a predictive methodology applicable to geothermal exploration in analogous basement domains. In our case, it has shown that the northern end of the Margeride fault zone could represent an area that needs to be explored further to assert its high geothermal potential. Our numerical models will increase understanding of how fault width and geometry impact the geothermal potential of the Margeride fault zone and similar areas in crystalline basement.

A serious issue with geothermal power plants is the loss of production and decline in power plant efficiency. Scaling, also known as mineral precipitation, is one of the frequently-observed issue that causes this loss and decreasing efficiency. It is heavily observed in the production wells when the geothermal fluid rises from the depths due to a change in the fluid’s physical and chemical properties. Scaling issue in geothermal power plants result in significant output losses and lower plant effectiveness. In rare instances, it might even result in the power plant being shut down. The chemistry of the geothermal fluid, non-condensable gases, pH, temperature and pressure changes in the process from production to reinjection, power plant type and design, and sometimes the materials used can also play an active role in the scaling that will occur in a geothermal system. ICP–MS was used to evaluate the chemical properties of the fluids. On the other hand, XRD, XRF and SEM were used to investigate the chemical and mineralogical compositions of the scale samples in analytical methods. For the numerical approach, PhreeqC and GWELL codes were used to follow the chemical reactivity of the geothermal fluid in Tuzla production well. The novelty of this study is to determine potential degassing point and to characterize the mineralogical assemblage formed in the well because of the fluid composition, temperature and pressure variations. During production, geothermal fluids degas in the wellbore. This causes a drastic modification of the chemistry of the Tuzla fluids. This is why it is focused the calculations on the nature of the minerals that are able to precipitate inside the well. According to simulation results, the degassing point is estimated to be about 105 m depth, consistent with the field observations. If a small quantity of precipitated minerals is predicted before the boiling point, degassing significantly changes the fluid chemistry, and the model predicts the deposition of calcite along with smaller elements including galena, barite, and quartz. The simulation results are consistent with the mineral composition of scaling collected in the well.

Geothermal energy is a sustainable resource for power generation, particularly in Yemen. Efficient utilization necessitates accurate forecasting of subsurface temperatures, which is challenging with conventional methods. This research leverages machine learning (ML) to optimize geothermal temperature forecasting in Yemen’s western region. The data set, collected from 108 geothermal wells, was divided into two sets: set 1 with 1402 data points and set 2 with 995 data points. Feature engineering prepared the data for model training. We evaluated a suite of machine learning regression models, from simple linear regression (SLR) to multi-layer perceptron (MLP). Hyperparameter tuning using Bayesian optimization (BO) was selected as the optimization process to boost model accuracy and performance. The MLP model outperformed others, achieving high (text {R}^{2}) values and low error values across all metrics after BO. Specifically, MLP achieved (text {R}^{2}) of 0.999, with MAE of 0.218, RMSE of 0.285, RAE of 4.071%, and RRSE of 4.011%. BO significantly upgraded the Gaussian process model, achieving an (text {R}^{2}) of 0.996, a minimum MAE of 0.283, RMSE of 0.575, RAE of 5.453%, and RRSE of 8.717%. The models demonstrated robust generalization capabilities with high (text {R}^{2}) values and low error metrics (MAE and RMSE) across all sets. This study highlights the potential of enhanced ML techniques and the novel BO in optimizing geothermal energy resource exploitation, contributing significantly to renewable energy research and development.