Search Results (300)

Exploitation of deep (>4000 m) tight geothermal reservoirs is constrained by low native permeability and premature thermal breakthrough, limiting sustainable heat recovery. Here, we investigate THM (thermo–hydro–mechanical) controls on fluid flow and heat transport during cold-water reinjection in deep tight sandstone reservoirs through an integrated framework linking two-dimensional mechanistic analysis with three-dimensional field-scale modeling. A two-dimensional thermo-poroelastic model reveals that strong thermal contrasts induced by cold-fluid injection cause contraction of the rock framework and transient pore-space dilation under confinement, producing short-term permeability enhancement. This process alters local flow capacity and redirects early cold-front migration, with persistent impacts on subsequent heat transport. Field-scale simulations further quantify the coupled effects of well spacing and reinjection temperature on thermal breakthrough, defined as a 1 °C decline in production-well temperature. Increased well spacing delays cold-front arrival and significantly retards breakthrough, whereas lower reinjection temperature enhances early heat extraction but accelerates convective transport, leading to earlier breakthrough. The combination of thermally enhanced permeability and intensified convection promotes preferential flow channels, increasing breakthrough risk. Balancing thermal-breakthrough delay against the heat-extraction driving force, the simulations delineate a favorable engineering window for the investigated conditions and clarify how cooling-sensitive permeability evolution affects preferential flow and reservoir-scale thermal response. Full article

Renewable energy sources, such as solar thermal and photovoltaic, geothermal, biomass, hydropower, and wind, offer significant sustainability advantages. Yet the sector still faces difficulties in several areas that tend to reduce the efficiency of these new energy forms. Some of these challenges include inconsistent electricity supply, the diffuse nature of renewable energy sources, which makes them difficult to exploit, and the inconsistent and unpredictable nature of electricity supply, which has repercussions for renewable energy markets. Although Industry 4.0 is inherently energy-intensive, its positive contribution to renewable energy systems may outweigh its costs. Consequently, this study conducts a scoping review on the role of digital technologies in renewable energy systems. It focuses on open-access conference papers, journal articles, and book chapters published between 2020 and 2026, selected from scientific platforms and databases such as IEEE Xplore, ScienceDirect, SpringerLink, and Scopus. A multi-stage screening process and a summary sheet for a set of 89 selected articles were produced to extract the necessary information. The results show that Industry 4.0 influences renewable energy systems at the design and installation stage in predictive maintenance, efficient management, and energy security. Meanwhile, Industry 4.0 in renewable energy systems still faces negative externalities that can be categorised as political, financial, infrastructural, environmental, human, security, and technological. To address these challenges, which tend to become entangled in cycles of negative reinforcement, the paper suggests defining standardised, clear, strict, and stable frameworks at the political, legal, regulatory, and environmental levels to overcome most challenges associated with the digital transformation of renewable energy. The study also recommends flexible, inclusive strategic planning that accounts for the digital maturity of the renewable energy system. From these perspectives, the study contributes to the literature by addressing the role of Industry 4.0 technologies in renewable energy systems from a strategic and coordinated perspective, from both human and technological standpoints. It also offers managerial and policy implications by supporting innovation in renewable energy systems on the one hand and contributing to policy and regulatory decision-making that favour their growth on the other. Full article

U-shaped well geothermal energy exploitation has become a key pathway for sustainable energy development, valued for its clean and stable attributes. However, constrained by the limited heat transfer capacity between the wellbore and traditional circulating water, the thermal extraction efficiency of the circulating fluid in the U-shaped well remains difficult to breakthrough, severely hindering the large-scale application. This work conducts a study on the optimization of the thermal conductivity performance of circulating working fluids based on water-phase dispersed nanoparticles, aiming to explore efficient heat transfer methods for the circulating working fluids in geothermal reservoir U-shaped wells. The finite element simulation is employed to analyze the influence of Al₂O₃ nanoparticle concentration (0–5%) and injection rate (4000–9000 m³/d) on thermal conductivity performance and flow characteristics. The results demonstrate that the Al₂O₃-H₂O nanofluid with a particle size of 10 nm and a concentration of 5% exhibits the optimal heat transfer performance. Under the optimization objective of maximizing net heat output with the pipe-velocity safety constraint satisfied, when the injection rate is 5000 m³/d, the heat extraction efficiency is improved by 21.31% compared with that of pure water. This work may provide theoretical data for efficient geothermal exploitation. Full article

The open-source Python package Geoloop introduces a novel, semi-analytical model for predicting the performance of deep (>500 m depth) vertical borehole heat exchangers (BHEs), with a focus on capturing depth-dependent variations in subsurface thermal properties, i.e., geothermal gradient and thermal conductivity. Conventional computationally efficient semi-analytical models based on load-aggregation of g-functions often assume uniform subsurface thermal properties. Geoloop addresses this gap by implementing a vertically stacked approach, allowing for realistic simulation of depth-variability in both the subsurface and borehole material properties. The model is benchmarked in the shallow domain against standard depth-uniform g-function implementations (up to 100 m depth) and for deeper conditions with a numerical finite volume model, demonstrating strong agreement and validating its accuracy and efficiency. Simulations for typical Dutch conditions show that deeper BHEs (up to 2000 m) can achieve significantly higher thermal power supply than shallower systems, and results in terms of resulting inlet/outlet temperatures for given heat extraction rates can strongly deviate (>4 °C) from results obtained by depth-uniform assumptions in thermal properties. Application of the model to the Dutch context reveals a non-linear increase in heat extraction potential with depth, surpassing values assumed in common practice by Dutch industry. The results highlight the importance of considering local geological heterogeneity and depth-dependent properties for accurate deep borehole heat exchanger (BHE) performance assessment and system optimization. Geoloop thus offers a robust, versatile platform for advancing the design and analysis of deep vertical BHE systems. Full article

Engineered geothermal system (EGS) cross-well flow of 30 L/s producing heat at a rate of Q~20 MW for 30 days was achieved by the UtahForge project in 2024. The cross-well flow doublet measured ℓ~400 m in length at L~100 m vertical offset. A first-order question is how sustainable the doublet’s 20 MW heat extraction is. Where once the answer would be framed in terms of pipe-like cubic-law flow along stress-aligned fault-scale planar heat exchange surfaces, UtahForge flow data rule out this heat exchange picture. The EGS flow data indicate aquifer-like volumetric cross-well flow with heat exchange at the grain scale. More specifically, the EGS flow data indicate no cross-well flow for a dozen hydrofrack attempts, while the 30 L/s flow occurred when the 400 m doublet wells were rendered effectively open to the crustal formation by drilling out all hydrofrack gear. An essential further observation is that the producer well flowed at only 70% of the injector rate: 30% of injected fluid was lost to flow heterogeneity in the cross-well volume. A four-step deconstruction of these observations explicitly characterizes the flow heterogeneous volume: (i) flow stimulation of the cross-well volume, (ii)wellbore-centric flow in/out of cross-well volume along the 400 m open well reach, (iii) heat advection in the cross-well volume, and (iv) sustainability-specific heat conduction into the cross-well volume. EGS stimulation process step (i) is attested by microseismic emissions (Meqs) registered on downhole sensors. Meq size and spatial correlations in turn reflect the flow heterogeneity of the cross-well volume. EGS step (iv), crustal heat conduction sustainability, is approximated by assuming radial heat energy extraction at rate Q/ℓ by a central line-sink of radius R < L/2. The line-sink analytic solution yields heat reservoir sustainability of ~3–10 years. Greater sustainability at Q/ℓ rate requires larger cross-well offsets L. The intimate relation between fluid flow and seismic emissions enables downhole seismic sensor data to image EGS flow stimulation activity. The future of EGS heat extraction depends to a large degree on feasible sizes of cross-well offset L in the flow-heterogeneous crust. Full article

Numerical simulation is an effective method for studying groundwater flow and heat transfer in geothermal energy projects. Describing the characteristics of thermal plumes is important for operational planning of geothermal energy projects. In contrast to shallow geothermal system, the injection temperature differs significantly from the natural temperature of thermal reservoir in high-temperature geothermal projects, which leads to changes in fluid density and dynamics viscosity. The purpose of this paper is to investigate the impacts of temperature-induced changes in density and dynamics viscosity on simulation. The Enhanced Geothermal System (EGS) in North Jiangsu Basin, China, is taken as a case project. Based on the theory of groundwater flow and heat transfer in porous-fracture dual medium, a numerical model of EGS is established to predict the thermal performance. The density and the dynamics viscosity in the model were set as either constant or temperature-dependent to simulate the hydraulic head and temperature of the production well. The influence of temperature-induced changes in density and dynamics viscosity on the simulation was quantitatively studied. The results show that temperature-induced change in dynamics viscosity has a greater impact on the simulation, with deviation in hydraulic head exceeding 20% if the dynamics viscosity is assumed constant. The temperature-dependent variation in viscosity should be incorporated into the simulation process to improve the accuracy of the calculation. In practice, EGS projects should maximize the temperature differential between produced and injected water. The increased viscosity of lower-temperature circulation water extends its residence time within the system, thereby facilitating more thorough heat extraction. This research enhances our understanding of the role of the temperature in groundwater flow and heat transfer within EGS. Full article

Urban heating systems continue to rely heavily on fossil fuels, driving significant CO₂ emissions and underscoring the need for scalable renewable alternatives. This study evaluates a solar-assisted aquifer thermal energy storage (ATES) system for sustainable urban heating, operating within a relatively deep aquifer. A numerical model of the Mannville aquifer is developed to simulate charge–discharge cycles in a relatively deep open-loop ATES system, examining subsurface temperature evolution, storage efficiency, and long-term thermal stability under Canadian climatic conditions. Modeling results indicate that such aquifers act as an effective thermal buffer for solar energy storage operations, smoothing seasonal temperature fluctuations and stabilizing heat production. Surplus solar thermal energy injected during low-demand periods significantly reduces long-term temperature decline and preserves thermal availability for winter extraction. Balancing contributions from solar and aquifer storage maintains system efficiency during peak demand while improving overall thermal management. The integrated approach enhances renewable energy utilization, reduces reliance on conventional heating systems, and strengthens the resilience of urban energy networks. Our findings demonstrate that coupling solar thermal input with geothermal heat storage in relatively deep aquifers offers a practical pathway for advancing sustainable urban heating in cold-climate regions. The modeling framework provides a foundation for optimizing seasonal storage strategies and guiding the design of hybrid solar–geothermal systems for large-scale urban applications. Full article

Geothermal energy extraction, hydrocarbon recovery, and CO₂ geological sequestration are frequently hindered by interfacial barriers and slow mass transfer. While high-power ultrasound offers a sustainable, purely physical method for reservoir stimulation, its field effectiveness remains debated because traditional macroscopic experiments fail to isolate mechanisms like acoustic streaming and cavitation. This review systematically examines acoustic mechanisms in porous media via microfluidic visualization, focusing on pore-scale fluid dynamics during enhanced oil recovery, hydrate dissociation, and CO₂ sequestration. Microscopic evidence reveals that fluid transport mechanisms depend heavily on pore geometry and local acoustic intensity. In wider channels, nonlinear acoustic flow provides sustained, directed convection to strip away concentration boundary layers; in narrow throats, microjets and pulsed stresses generated by transient cavitation are responsible for physically breaking capillary barriers. The spatiotemporal synergy of these mechanisms is critical for multiphase fluid transport in tight porous networks. Pore geometry serves not only as the application context but also as a core physical variable. To translate microfluidic results into reservoir-scale applications, future research must address two-dimensional simplifications, thermodynamic discrepancies under high-temperature and high-pressure conditions, and bubble cluster interactions, alongside the development of adaptive frequency-modulated control and multiscale computational models. Full article

The rapid growth of battery energy storage and electric vehicles has increased lithium demand and intensified the attention given to the environmental performance of alternative extraction pathways. Conventional life cycle assessments (LCA) of lithium production typically report midpoint indicators in physical units, which limits cross-category comparison and reduces their usefulness for economic and policy analysis. This study presents a comparative monetized LCA of lithium carbonate equivalent (LCE) production from three pathways: solar brine evaporation, hard-rock spodumene mining, and geothermal brine recovery. Using the TRACI 2.1 midpoint results reported in a prior LCA, six impact categories—global warming, smog formation, acidification, respiratory effects, carcinogenic toxicity, and non-carcinogenic toxicity—are converted into monetary values through a benefit-transfer, damage-cost approach. Total environmental external costs are estimated at USD 11.85/kg LCE for solar brine evaporation, USD 9.45/kg LCE for spodumene mining, and USD 4.11/kg LCE for geothermal brine recovery (all USD amounts are expressed in $2025 unless otherwise mentioned). Smog formation contributes more than 80% of the total monetized damages across all pathways, while toxicity-related impacts account for a smaller share than implied by the normalized midpoint results. Monetization changes the relative ranking of the solar brine and spodumene pathways, while indicating that geothermal brine recovery has the lowest monetized external cost among the impact categories evaluated. These findings show that monetized LCA can complement conventional midpoint assessment and provide more decision-relevant insights for policy and economic evaluation. Full article

To overcome the rapid expansion of the drawdown cone, severe inter-well interference, and high operating costs caused by independent geothermal well operation, this study investigated the coordinated optimal scheduling of geothermal water extraction. Fifteen geothermal production wells in the main urban area of Kaifeng City were selected as the study case. The intake intervals of these wells are located at depths of 1020 to 1330 m. Based on the exploitable yield of the geothermal reservoir, user water demand, and well layout, a management model for coordinated scheduling was developed. Design drawdown, water demand, and heating capacity were used as constraints. The objectives were to minimize operating cost, nodal drawdown, and drawdown interference between wells. The results from several optimization algorithms show that the improved Cheetah Optimization Algorithm converged faster and produced more consistent solutions. Compared with the preoptimization scheme, the optimized scheme reduced total operating cost by 31.64%, total drawdown in the study area by 69.5%, and the sum of inter-well drawdown interference by 34.7%. This study provides useful support for selecting efficient optimization algorithms and offers a basis for the scientific development, utilization, and protection of geothermal water resources. Full article

Introduction: Efficient geothermal energy extraction has the potential to significantly alleviate the shortage of fossil energy, but low extraction efficiency and an insufficiently understood extraction mechanism remain key bottlenecks hindering its large-scale deployment. Method: This study develops a fluid–solid coupled numerical model based on the intrinsic physical properties of geological reservoirs to systematically analyze the energy extraction characteristics of geothermal systems. Simultaneously, the effects of key geological factors on fluid flow behavior within geothermal reservoirs are investigated. Furthermore, molecular dynamics simulations are employed to elucidate the microscopic mechanisms by which driving fluids facilitate geothermal energy extraction. Results: The results demonstrate that the thermo-hydraulic–mechanical (THM) numerical model was validated through a comparison with benchmark data reported in previous studies, exhibiting a high degree of agreement with geothermal extraction performance. The model further confirms that heat transport in the geothermal reservoir is characterized by a pronounced “tongue-in” isotherm pattern during the extraction process. Discussion: Lower initial temperatures of the driving fluid lead to more rapid geothermal energy extraction compared with higher initial temperatures, and the “tongue-in” phenomenon becomes increasingly pronounced as the initial injection temperature decreases. Moreover, increased injection pressure significantly enhances geothermal energy extraction efficiency; however, reduced pressure differentials markedly suppress the development of the “tongue-in” pattern and decrease reservoir permeability. In addition, water used as a heat-driving fluid achieves higher thermal extraction efficiency than water, while simultaneously exerting a stronger moderating effect on the permeability evolution of geothermal reservoirs. Conclusions: The simulation results obtained from the thermo-hydraulic-mechanical (THM) numerical model provide fundamental data to support the efficient development of geothermal reservoirs, while the associated analyses offer valuable insights into the selection of appropriate driving fluids for reservoirs with distinct geological characteristics. Full article

Geothermal silica has emerged as a promising and underutilised precursor for silicon-based lithium-ion battery anodes. Geothermal silica can be recovered from brines, scales, and solid residues generated during geothermal energy production, creating an opportunity to valorise existing waste streams while mitigating silica-scaling problems. This review examines the formation, availability, and material characteristics of geothermal silica, with particular emphasis on its high silica content, commonly reported in the range of ~50–98 wt% in solid geothermal residues, as well as its generally amorphous nature and porous structure. It then evaluates the main processing steps required to convert geothermal silica into battery-relevant silicon, including extraction, purification, and silica-to-silicon reduction, with particular focus on magnesiothermic reduction. Among the available routes, methods that provide improved impurity control while preserving porous or amorphous precursor structures appear most relevant for achieving favourable electrochemical performance. Recent comparative findings indicate that geothermal silica can, in some cases, be competitive with biomass-derived silica sources in terms of purity, composition, and morphology, although these advantages are not universal and depend on source-specific chemistry, impurity profile, and processing conditions. Reported electrochemical studies further show that geothermal-silica-derived silicon and silica-based composites can deliver electrochemically relevant capacities, in some cases exceeding the theoretical capacity of graphite (~372 mAh g⁻¹), although performance varies significantly across studies. In addition, specific surface areas of ~50–150 m² g⁻¹ reported for some geothermal silica materials may support further silicon processing and influence electrochemical behaviour. Overall, geothermal silica represents a technically relevant and sustainability-oriented pathway toward silicon-based anode materials; however, further work is needed on source consistency, impurity management, structural control, long-term cycling stability, and scalable production. Full article

Thermal infrared remote sensing offers a cost-effective means of regional geothermal reconnaissance, yet a fundamental challenge remains: isolating the weak geothermal surface signal (typically 1–3 °C) from dominant surface noise introduced by seasonal temperature cycles (annual amplitude > 20 °C), topographic variability, land cover heterogeneity, and irregular cloud-affected satellite sampling. Conventional single-scene or arithmetic-mean approaches are highly susceptible to these confounding factors and frequently produce pseudo-anomalies that obscure genuine geothermal targets. To overcome this limitation, we propose a physics-based time-series framework in which a nonlinear annual temperature variation model, T(t) = T0 + A·sin(2πt/τ + φ), is fitted to multi-temporal Landsat 8 thermal infrared data via the Levenberg–Marquardt algorithm. Applied to ~50 cloud-free scenes (2021–2022) processed on the Google Earth Engine over the Shanxi Graben System, northern China, the model simultaneously retrieves the background temperature parameter T0 and seasonal amplitude A—two physically interpretable quantities that encode distinct geothermal signatures more robustly than simple temporal statistics. Sub-regional corrections for the elevation (−4 °C/100 m above 800 m), aspect (R² > 0.95 in piecewise linear segments), and slope further suppress topographic pseudo-anomalies prior to anomaly extraction. Over known high-temperature geothermal fields (Tianzhen and Yanggao; >100 °C at 100 m depth), the method reveals clear T0 offsets of +1–2 °C (3–5% relative) and amplitude deficits of ~2 K (5–10% relative) relative to the background, with model-fitted T0 values averaging ~2 °C higher than arithmetic means due to the correction for seasonal sampling bias. Combined with 5 km fault-proximity buffers, extracted anomaly zones align well spatially with known geothermal sites and major structural corridors of the graben system. However, deeper low-temperature systems (45–50 °C at 300–500 m depth) produce ambiguous signals below the ~1.5 K detection threshold, indicating inherent limitations for deeply buried resources. The fully reproducible, training-data-free workflow is implementable via open satellite archives and cloud computing platforms, making it a transferable low-cost tool for structurally controlled geothermal reconnaissance across extensional basins worldwide. Full article

With the continuous development of drilling and reservoir stimulation technologies, the drilling depth of enhanced geothermal system projects is getting deeper and deeper, and the surrounding rock stress of dry hot rock reservoirs is also increasing. Therefore, it has become an inevitable demand for geothermal exploitation to study the evolution law of fracture seepage characteristics of granite under high temperature and ultra-high pressure. To reveal the evolutionary patterns of seepage characteristics in deep-seated hot dry rock fractures, an independently developed ultra-high pressure rock triaxial mechanical testing system was employed to investigate the seepage characteristics of fractured granite under varying temperatures (25–150 °C) and triaxial stresses (50–100 MPa). The study explores the influence of temperature on the seepage characteristics of granite fractures under ultra-high triaxial stress conditions. The results indicate that: (1) In the temperature range of 25–125 °C, as the rock temperature increases, the permeability of the Specimens showed a continuously decreasing trend due to the effect of thermal expansion. (2) In the temperature range of 125–150 °C, as the rock temperature increases, the permeability continues to decrease under low triaxial stress (50 MPa). However, under high triaxial stress (75 MPa) and extremely high triaxial stress (100 MPa), the permeability shows a slight increase instead. This phenomenon is attributed to free surface dissolution. (3) Quantitative analysis of the mesoscopic morphological data of the rock fracture surfaces after testing, combined with SEM images from scanning electron microscopy, confirms that within the high-temperature range of 125–150 °C, the differing levels of triaxial stress determine the variation in the dominant mechanism governing the evolution of the Specimen fracture surfaces, which in turn leads to the divergence in the trend of their permeability changes. Full article

Electricity demand in the United States is steadily increasing due to rapid technological growth, especially the expansion of AI data centers and electric vehicles, which are becoming major power consumers. At the same time, rising renewable energy integration, changing weather patterns, and the deployment of battery energy storage systems are increasing variability and complexity in grid operations. These evolving conditions require advanced forecasting methods to ensure reliability and efficiency, as traditional statistical and machine learning models struggle with nonlinear and temporal dependencies. To address these challenges, this study proposes a hybrid deep learning framework that combines convolutional neural networks, long short-term memory, and bidirectional LSTM models to forecast electricity generation across both conventional and renewable energy sources. The framework incorporates seasonal-trend decomposition using loess to extract trend, seasonal, and residual components, enhancing the learning of multi-scale temporal patterns. A key contribution of this work is the development of a unified, source-specific forecasting system in which each energy source is assigned its best-performing hybrid architecture. The proposed framework achieves superior accuracy, with the CNN-Bi-LSTM model yielding the best total power results (MAPE 2.60%, RMSE 13,745 MWh, MAE 9542 MWh), while Bi-LSTM models excel for wind, biomass, geothermal, and nuclear. This enables scalable, high-precision national-level forecasting. Full article