Geothermal Energy最新文献

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.

Under the influence of diapir structure, the formation pressure system is complicated. The characteristics of high temperature and high pressure are obvious, the prediction is difficult, and complex accidents such as well kick and leakage are frequent, which seriously restrict the efficient development of oil and gas resources. Therefore, taking Yinggehai Basin in China as an example, combined with the evolution characteristics of diapir structure, the influence of diapir structure on abnormal high-pressure, wellhole collapse and fracture is analyzed. Three pressure calculation methods are selected, and the distribution rules of pressures and safety density window are analyzed, too. The results show that the diapir structure and its associated fault not only constitute the fluid transport system, but also make the deep overpressure transfer upward and accumulate into high pressure in the shallow formation, and the development of the associated fault destroy the integrity of the formation rock and reduce the strength of the rock. The upwelling of hot fluid changes the local geothermal conditions, reduces the hydrocarbon generation threshold of shallow source rocks, promotes the evolution of clay minerals, causes hydrothermal expansion, and enhances the shallow high pressure. In high-temperature environment, the cooling effect of drilling fluid will produce heating stress, change the stress distribution around the wellhole, and increase the risk of wellbore instability. Additionally, under the influence of diapir structures, the pore pressure in deep formations increases, while the fracture pressure decreases, resulting in a significantly narrowed safe density window. The safety density window width generally presents a half-spindle shape, and with the increase of depth, the window width increases first and then decreases.

Aquifer thermal energy storage (ATES) has great potential to mitigate CO₂ emissions associated with the heating and cooling of buildings and offers wide applicability. Thick productive aquifer layers have been targeted first, as these are the most promising hydrogeological context for ATES. Regardless, there is currently an increasing trend to target more complex aquifers such as low-transmissivity and alluvial aquifers or fractured rock formations. There, the uncertainty of subsurface characteristics and, with that, the risk of poorly performing systems is considerably higher. Commonly applied strategies to decide upon the ATES feasibility and well design standards for optimization need to be adapted. To further promote the use of ATES in such less favorable aquifers an efficient and systematic methodology evaluating the optimal conditions, while not neglecting uncertainty, is crucial. In this context, the distance-based global sensitivity analysis (DGSA) method is proposed. The analysis focuses on one promising thick productive aquifer, first used to validate the methodology, as well as a complex shallow alluvial aquifer. Through this method, multiple random model realizations are generated by sampling each parameter from a predetermined range of uncertainty. The DGSA methodology validates that the hydraulic conductivity, the natural hydraulic gradient and the annual storage volume dominate the functioning of an ATES system in both hydrogeological settings. The method also advances the state of the art in both settings. It efficiently identifies most informative field data ahead of carrying out the field work itself. In the studied settings, Darcy flux measurements can provide a first estimate of the relative ATES efficiency. It further offers a substantiated basis to streamline models in the future. Insensitive parameters can be fixed to average values without compromising on prediction accuracy. It also demonstrates the insignificance of seasonal soil temperature fluctuations on storage in unconfined shallow aquifers and it clarifies the thermal energy exchange dynamics directly above the storage volume. Finally, it creates the opportunity to explore different storage conditions in a particular setting, allowing to propose cutoff criteria for the investment in ATES. The nuanced understanding gained with this study offers practical guidance for enhanced efficiency of feasibility studies. It proves that the DGSA methodology can significantly speed up the development of ATES in more complex hydrogeological settings.

Purpose

In high-temperature geothermal fields, interpretation of the dynamic two-phase state inside the production wells under different wellhead conditions are important to effectively use the geothermal heat source. Therefore, the corresponding wellbore models must have the capability to simulate transient flow and energy state in geothermal wellbores, as well as advective and conductive heat and mass interactions with surrounding formation.

Methods

In this study, a transient two-phase wellbore model is developed and implemented in the open source software OpenGeoSys, to simulate both flow and energy state in the wellbore, as well as advective and conductive heat and mass interactions with surrounding formation. The model is first verified against analytical solutions and numerical results from the open-source simulator FloWell. The model is then further validated with well logging data from the Yangyi geothermal field in Tibet, China.

Results

Based on the simulation results of the parametric analysis, the conductive heat loss of the high-velocity geothermal production well in the Yangyi geothermal field is found to be limited and the influence can be safely neglected after 8 h of discharge. The flash point location in the wellbore moves upwards for 112 m along with the decrease in fluid enthalpy by 200 kJ/kg. In the wellbore shut-in process, the wellhead pressure decreases with decreasing velocity, while the location of the flash point does not change much. After wellbore shut-in, a two-phase state still exists in the closed wellbore, and the temperature profile is dominated by conductive heat exchange with the surrounding formation. Taking into account the impact of the feed zone, the mass flow rate of the ZK203 well in the Yangyi geothermal field increases from 122.87 to 126.26 t/h when the wellhead pressure decreases from 1.26 to 1.18 MPa.

Conclusion

The open-source two-phase wellbore model developed and implemented in this work provides preliminary insights into the transition and evolution of the two-phase state in high-temperature production wells considering advective and conductive interactions with the surrounding formation.