Search Results (343)

Soil thermal conductivity (λ) is a critical parameter governing heat transfer in geothermal exploitation, nuclear waste disposal, and landfill engineering. This study explores the thermal conductivity characteristics of silty clay and develops a prediction model using the actively heated fiber-optic method based on fiber Bragg grating technology. Tests analyze the effects of particle content (silt and sand), dry density, moisture content, organic matter (sodium humate and potassium humate), and salt content on λ. Results show λ decreases with increasing silt, sand, and organic matter content, while it increases exponentially with dry density. The critical moisture content is 50%, beyond which λ declines, and λ first rises then falls with salt content exceeding 2%. Sensitivity analysis reveals dry density is the most influential factor, followed by sodium humate and silt content. A modified Johansen model, incorporating shape factors correlated with influencing variables, improves prediction accuracy. The root mean squared error decreases to 0.087, and coefficient of determination increases to 0.866. The study provides an accurate method for measuring thermal conductivity and enhances understanding of the heat-transfer mechanism in silty clay. Full article

Since the hydrogen-production process is not yet fully efficient, this paper proposes a poly-generation system that is driven by a geothermal energy source and utilizes a combined Kalina/organic Rankine cycle coupled with an electrolyzer unit to produce, simultaneously, power and green hydrogen in an efficient way. A comprehensive thermodynamic analysis and an exergetic evaluation are carried out to assess the effect of key system parameters (geothermal temperature, high pressure, ammonia–water concentration ratio, and terminal thermal difference) on the performance of concurrent production of power and green hydrogen. Thereby, two configurations are investigated with/without the separation of turbines. The optimal ammonia mass fraction of the basic solution in KC is identified, which leads to an overall optimal system performance in terms of exergy efficiency and green hydrogen production rate. In both configurations, the optimal evaluation is made possible by conducting a genetic algorithm optimization. The simulation results without/with the separation of turbines demonstrate the potential of the suggested cycle combination and emphasize its effectiveness and efficiency. Exemplary, for the case without the separation of turbines, it turns out that the combination of ammonia–water and MD2M provides the best performance with net power of 1470 kW, energy efficiency of 0.1184, and exergy efficiency of 0.1258 while producing a significant green hydrogen amount of 620.17 kg/day. Finally, an economic study allows to determine the total investment and payback time of $3,342,000 and 5.37 years, respectively. The levelized cost of hydrogen (LCOH) for the proposed system is estimated at 3.007 USD/kg H₂, aligning well with values reported in the literature. Full article

To enhance the development efficiency of medium–deep geothermal resources in cold regions, this study focuses on a coaxial borehole heat exchanger (CBHE) located in Dapuzi Town, Xining City, Qinghai Province. Based on field-scale heat exchange experiments, a three-dimensional numerical model of the CBHE was developed using COMSOL Multiphysics 6.2, incorporating both conductive heat transfer in the surrounding geological formation and convective heat transfer within the wellbore. The model was calibrated and validated against measured data. On this basis, the effects of wellhead injection flow rate, injection temperature, and the thermal conductivity of the inner pipe on heat exchange performance were systematically analyzed. The results show that in cold regions with high altitudes (2000–3000 m) and medium–deep low-temperature geothermal reservoirs (68.8 °C), using a coaxial heat exchange system for space heating delivers good heat extraction performance, with a maximum average power output of 282.37 kW. Among the parameters, the injection flow rate has the most significant impact on heat extraction. When the flow rate increases from 10 m³/h to 30 m³/h, the heat extraction power increases by 57.58%. An increase in injection temperature helps suppress thermal short-circuiting and improves the effluent temperature, but excessively high temperatures lead to a decline in heat extraction. Additionally, increasing the thermal conductivity of the inner pipe significantly intensifies thermal short-circuiting and reduces overall heat exchange capacity. Under constant reservoir conditions, the thermal influence radius expands with both depth and operating time, reaching a maximum of 10.04 m by the end of the heating period. For the CBHE system in Dapuzi, maintaining an injection flow rate of 20–25 m³/h and an injection temperature of approximately 20 °C can achieve an optimal balance between effluent temperature and heat extraction. Full article

This study investigates the exergoeconomic performance of a milk pasteurization system powered by geothermal energy, operating across geothermal source temperatures (GSTs) ranging from 80 °C to 110 °C. The system uses geothermal heat as its primary energy source, while the cooling process is supported by a vapor compression refrigeration cycle driven by electricity generated through an Organic Rankine Cycle (ORC). The analysis was carried out in three stages: determining system parameters for each GST level, conducting detailed energy and exergy analyses, and performing an exergoeconomic evaluation using the specific exergy costing (SPECO) method. The results show that both energy and exergy efficiencies decline as GST increases. Energy efficiency varies between 88.30% and 78.53%, while exergy efficiency ranges from 72.86% to 58.02%. In parallel, unit-specific manufacturing costs increase with higher GST. Electricity production costs range from 610 to 900 USD·MWh⁻¹, and the cost of pasteurized milk varies between 3.76 and 6.53 USD·ton⁻¹. These findings offer practical insights into how geothermal source temperature affects the thermodynamic and economic performance of such systems, contributing to the broader understanding of sustainable dairy processing technologies. Full article

We have developed a generalisable machine learning framework for reservoir quality prediction in deeply buried clastic systems. Applied to the Lower Jurassic deltaic sandstones of the Tilje Formation (Halten Terrace, North Sea), the approach integrates sedimentological facies modelling with mineralogical and petrophysical prediction in a single workflow. Using supervised Extreme Gradient Boosting (XGBoost) models, we classify reservoir facies, predict permeability directly from standard wireline log parameters and estimate the abundance of porosity-preserving grain coating chlorite (gamma ray, neutron porosity, caliper, photoelectric effect, bulk density, compressional and shear sonic, and deep resistivity). Model development and evaluation employed stratified K-fold cross-validation to preserve facies proportions and mineralogical variability across folds, supporting robust performance assessment and testing generalisability across a geologically heterogeneous dataset. Core description, point count petrography, and core plug analyses were used for ground truthing. The models distinguish chlorite-associated facies with up to 80% accuracy and estimate permeability with a mean absolute error of 0.782 log(mD), improving substantially on conventional regression-based approaches. The models also enable prediction, for the first time using wireline logs, grain-coating chlorite abundance with a mean absolute error of 1.79% (range 0–16%). The framework takes advantage of diagnostic petrophysical responses associated with chlorite and high porosity, yielding geologically consistent and interpretable results. It addresses persistent challenges in characterising thinly bedded, heterogeneous intervals beyond the resolution of traditional methods and is transferable to other clastic reservoirs, including those considered for carbon storage and geothermal applications. The workflow supports cost-effective, high-confidence subsurface characterisation and contributes a flexible methodology for future work at the interface of geoscience and machine learning. Full article

This study presents a methodology for estimating hydraulic conductivity (K) from well geophysical logs, with the aim of improving the parameterization of hydrogeological models in data-scarce regions. The lack of data poses a challenge for aquifer characterization, especially in contexts requiring integrated groundwater management. In such contexts, indirect methods can support estimation of key hydraulic parameters. The proposed methodology was applied to wells which penetrate Neogene–Quaternary hydrogeological units of the sedimentary aquifer system in the Middle Magdalena Valley, Colombia. Effective porosity was estimated from sonic and gamma ray logs, while temperature profiles were derived from the regional geothermal gradient and calibrated with field measurements. Hydraulic conductivity was estimated using an approach based on the Csókás method and validated through comparison with 131 pumping tests and alignment with the parameterization of a previously calibrated regional groundwater flow model. Pumping tests yielded geometric mean K values of 1.56 m/day in floodplain deposits (QFal), 1.36 m/day in U4, and 0.96 m/day in U3. K values from well logs ranged from 1.65 to 2.95 m/day, within the same order of magnitude. These findings support the proposed methodology as a viable alternative for the parameterization of numerical hydrogeological models in data-scarce environments. Full article

With the global low-carbon energy transition, accurate prediction of thermal and physical parameters of deep rock masses is critical for geothermal resource development. To address the insufficient generalization ability of machine learning models caused by scarce measured data on granite thermal conductivity, this study focused on granites from the Gonghe Basin and Songliao Basin in Qinghai Province. A data augmentation strategy combining cubic spline interpolation and Gaussian noise injection (with noise intensity set to 10% of the original data feature range) was proposed, expanding the original 47 samples to 150. Thermal conductivity prediction models were constructed using Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network(BPNN). Results showed that data augmentation significantly improved model performance: the RF model exhibited the best improvement, with its coefficient of determination R² increasing from 0.7489 to 0.9765, Root Mean Square Error (RMSE) decreasing from 0.1870 to 0.1271, and Mean Absolute Error (MAE) reducing from 0.1453 to 0.0993. The BPNN and SVM models also improved, with R² reaching 0.9365 and 0.8743, respectively, on the enhanced dataset. Feature importance analysis revealed porosity (with a coefficient of variation of 0.88, much higher than the longitudinal wave velocity’s 0.27) and density as key factors, with significantly higher contributions than longitudinal wave velocity. This study provides quantitative evidence for data augmentation and machine learning in predicting rock thermophysical parameters, promoting intelligent geothermal resource development. Full article

A reliable geothermal risk assessment methodology is key to any business decision. To be effective, it must be based on widely accepted principles, be easy to apply, be auditable, and produce consistent results. In this paper, we review the key characteristics of a geothermal project and propose a novel approach derived from risk and uncertainty definitions used in the hydrocarbon industry. According to the proposed methodology, the probability of success is assessed by estimating three different components. The first is the geological probability of success, which is the likelihood that the geological model on which the geothermal project is based is correct and that the key fundamental geological elements are present. The second, the temperature threshold, is defined as the probability that the fluid is above a certain reference value. Such a reference value is the one used to design the development. Such a component, therefore, depends on the end use of the geothermal resource. The third component is the commercial probability of success and estimates the chance of a project being commercially viable using the Reverse Enthalpy Methodology. Geothermal projects do not have a single parameter that represents their monetary value. Therefore, in order to estimate it, it is necessary to make an initial assumption that can be revisited later in an iterative manner. The proposed methodology works with either the capital expenditure of the geothermal facility (power plant or direct thermal use) or the drilling cost as the initial assumption. Varying the other parameter, it estimates the probability of having a net present value (NPV) higher than zero. Full article

This paper discusses a thorough analysis, as well as the design, of an environmentally friendly, single-stage Organic Rankine Cycle (ORC) system, particularly optimized for untapped geothermal applications in Pakistan that are secluded and off-grid, to tackle the severe energy crises choking this country and its resources. Keeping in mind its Global Warming Potential (GWP), as well as its performance in the ORC, r600a was chosen as the operating fluid. This study focuses on varying the temperature, pressure, and mass flow rate of not only the geothermal reservoir but that of the operating fluid in the ORC as well. The impacts of adjusting these parameters on the net power output, cycle efficiency, and component-wise exergy destruction, as well as the total exergy destruction, are examined extensively. Analyses of the component-wise exergy destruction found that the maximum exergy destruction occurred in the evaporator, whereas it was discovered that decreasing the condenser pressure below 350 kPa led to negative exergy destruction values, although the total exergy destruction remained positive. Full article

Combining Organic Rankine Cycles (ORC) with cooling cycles offers a promising approach to achieving greater outputs within a single system. In this study, a novel directly combined ORC-VCC system has been designed to not only meet the cooling demand using a geothermal heat source but also generate power. The proposed novel ORC-VCC system has been analyzed for its energetic performance using four selected fluids: R290, R600a, R601, and R1234ze(E). Parametric analysis has been conducted to investigate the effects of parameters of heat source temperature, heat source mass flow rate, cooling capacities, condenser temperature, ORC evaporator temperature, pinch point temperature difference and isentropic efficiencies on net power production. Among the working fluids, R290 has provided the highest net power production under all conditions in which it was available to operate. Additionally, the results have been analyzed concerning a reference cycle for comparative evaluation. The proposed novel cycle has outperformed the reference cycle in all investigated cases in terms of net power production such as demonstrating an improvement of approximately from 8.7% to 57.8% in geothermal heat source temperature investigations. Similar improvements have been observed over the reference cycle at lower heat source mass flow rates, where net power increases by up to 50.8%. Full article

The process of geothermal energy development may cause induced seismic activities, posing a potential threat to the sustainable utilization and safety of geothermal energy. To effectively evaluate the danger of induced seismic activities, this paper establishes an artificial neural network model and selects nine influencing factors as the input parameters of the neurons. Based on the results of induced seismic activity under different parameter conditions, a sensitivity analysis is conducted for each parameter, and the influence degree of each parameter on the magnitude of induced seismic activity is ranked from largest to smallest as follows: in situ stress state, fault presence or absence, depth, degree of fracture aggregation, maximum in situ stress, distance to fault, injection volume, fracture dip angle, angle between fracture, and fault. Then, the weights of each parameter in the model are modified to improve the accuracy of the model. Finally, through data collection and the literature review, the Pohang EGS project in South Korea is analyzed, and the induced seismic activity influencing factors of the Pohang EGS site are analyzed and evaluated using the induced seismic activity evaluation model. The results show that the induced seismicity are all located below 3.7 km (drilling depth). As the depth increases, the seismicity magnitude also shows a gradually increasing trend. An increase in injection volume and a shortening of the distance from faults will also lead to an increase in the seismicity magnitude. When the injection volume approaches 10,000 cubic meters, the intensity of the seismic activity sharply increases, and the maximum magnitude reaches 5.34, which is consistent with the actual situation. This model can be used for the induced seismic evaluation of future EGS projects and provide a reference for project site selection and induced seismic risk warning. Full article

In order to reduce gas consumption and increase the renewable energy proportion, this paper proposes a poly-generation system that couples geothermal, solar, and liquid natural gas (LNG) cold energy to produce steam, gaseous natural gas, and low-temperature nitrogen. The high-temperature flue gas is used to heat LNG; low-temperature flue gas, mainly nitrogen, can be used for cold storage cooling, enabling the staged utilization of the energy. Solar shortwave is used for power generation, and longwave is used to heat the working medium, which realizes the full spectrum utilization of solar energy. The influence of different equipment and operating parameters on the performance of a steam generation system is studied, and the multi-objective model of the multi-generation system is established and optimized. The results show that for every 100 W/m² increase in solar radiation, the renewable energy ratio of the system increases by 1.5%. For every 10% increase in partial load rate of gas boiler, the proportion of renewable energy decreases by 1.27%. The system’s energy efficiency, cooling output, and the LNG vaporization flow rate are negatively correlated with the scale of solar energy utilization equipment. The decision variables determined by the TOPSIS (technique for order of preference by similarity to ideal solution) method have better economic performance. Its investment cost is 18.14 × 10 CNY, which is 7.83% lower than that of the LINMAP (linear programming technique for multidimensional analysis of preference). Meanwhile, the proportion of renewable energy is only 0.29% lower than that of LINMAP. Full article

The global shift towards the use of renewable energy is essential to ensure sustainable development, and geothermal energy stands out as a suitable option that can support various cascading projects. Spent geothermal water (SGW) requires proper treatment to ensure that it does not become an environmental burden. Typically, companies often face the dilemma of choosing between discharging spent geothermal water (SGW) into surface waters or injecting it into rock masses, and the economic and environmental impacts of the decision made determines the feasibility of geothermal plant development. In this study, we aimed to comprehensively assess the technical, economic, and environmental feasibility of SGW injection into rock masses. To this end, we employed a comprehensive analytical approach using the Chochołów GT-1 geothermal injection borehole in Poland as a reference case. We also performed drilling and hydrogeological testing, characterized rock samples in the laboratory, and corrected hydrodynamic parameters for thermal lift effects to ensure accurate aquifer characterization. The results obtained highlight the importance of correcting hydrogeological parameters for thermal effects, which if neglected can lead to a significant overestimation of the calculated hydrogeological parameters. Based on our analysis, we developed a framework for assessing SGW injection feasibility that integrates detailed hydrogeological and geotechnical analyses with environmental risk assessment to ensure sustainable geothermal resource exploitation. This framework should be mandatory for planning new geothermal power plants or complexes worldwide. Our results also emphasize the need for adequate SGW management so as to ensure that the benefits of using a renewable and zero-emission resource, such as geothermal energy, are not compromised by the low absorption capacity of rock masses or adverse environmental effects. Full article

The central role of heating and cooling in energy transition has been recognised in recent years, especially with geopolitical developments since February 2022 which demand an acceleration in deploying local energy sources to increase the resilience of the energy sector. Geothermal energy is a promising and vital option to optimize heating and cooling systems, promoting sustainability of urban environments. To this end, a proper design is of paramount importance to guarantee the energy performance of the whole system. This work deals with the optimization of the technical and geometrical characteristics of borehole heat exchangers (BHEs) as part of a shallow geothermal plant that is assumed to be integrated in an already operating gas-fired DH grid. Thermal performances of three different configurations were analysed according to the geological information that revealed an aquifer at −36 m overlying a poorly permeable marly succession. Numerical simulations validated the geological, hydrogeological, and thermo-physical models by back-analysing the experimental results of a thermal response test (TRT) on a pilot 150 m deep BHE. Five-year simulations were then performed to compare 150 m and 36 m polyethylene 2U, and 36 m steel coaxial BHEs. The coaxial configuration shows the best performance both in terms of specific power (74.51 W/m) and borehole thermal resistance (0.02 mK/W). Outcomes of the study confirm that coupling the best geological and technical parameters ensure the best energy performance and economic sustainability. Full article

This paper explores the complex dynamics of mixed convective flow in a Casson fluid saturated in a non-Darcy porous medium, focusing on the influence of gyrotactic microorganisms, internal heat generation, and multiple convective mechanisms. Casson fluids, known for their non-Newtonian behavior, are relevant in various industrial and biological contexts where traditional fluid models are insufficient. This study addresses the limitations of the standard Darcy’s law by examining non-Darcy flow, which accounts for nonlinear inertial effects in porous media. The governing equations, derived from conservation laws, are transformed into a system of no linear ordinary differential equations (ODEs) using similarity transformations. These ODEs are solved numerically using a finite differencing method that incorporates central differencing, tridiagonal matrix manipulation, and iterative procedures to ensure accuracy across various convective regimes. The reliability of this method is confirmed through validation with the MATLAB (R2024b) bvp4c scheme. The investigation analyzes the impact of key parameters (such as the Casson fluid parameter, Darcy number, Biot numbers, and heat generation) on velocity, temperature, and microorganism concentration profiles. This study reveals that the Casson fluid parameter significantly improves the velocity, concentration, and motile microorganism profiles while decreasing the temperature profile. Additionally, the Biot number is shown to considerably increase the concentration and dispersion of motile microorganisms, as well as the heat transfer rate. The findings provide valuable insights into non-Newtonian fluid behavior in porous environments, with applications in bioengineering, environmental remediation, and energy systems, such as bioreactor design and geothermal energy extraction. Full article