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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Climate change is threatening vulnerable geographies including island nations, posing major economic and social risks. The development of renewables, specifically geothermal energy in the Hawaiian archipelago offers promising possibilities to achieve energy independence and security. However, geothermal development faces opposition from the Native Hawaiian community, stemming from Hawaiian cultural identity and beliefs that natural resources are akua (gods). Throughout Hawai'i's history Native Hawaiians have been marginalized in decision making processes, overlooking cultural implications of development. In this article we explore how to improve collaboration with Native Hawaiians to guide future geothermal energy development in Hawai'i. We conducted seven semi-structured interviews with individuals from three groups: (1) Native Hawaiians community members, (2) Native Hawaiian geothermal professionals, and (3) non-native geothermal professionals. Using a grounded theory approach, we discovered the interviews emphasized the status of geothermal development, feelings towards geothermal, perceived obstacles, knowledge gaps and recommendations to improve development. Interviews were manually transcribed and analyzed to identify similarities and differences in responses. Due to the limited number of interviews, our results are not statistically robust, but do offer insights for equitable culturally grounded geothermal energy development. Findings include six key themes. (1) Renewable energy is critical for Hawai'i's future sustainability. (2) The primary obstacles facing geothermal energy expansion are not cultural. The largest obstacles are regulatory and financial. (3) Reasons and opportunities to support Geothermal exploration and expansion exist. Geothermal provides Hawai'i a firm energy resource with a minimal ecological footprint. (4) Cultural implications surrounding geothermal are significant. (5) There is a need to rebuild collaboration and trust. Past developments failed to collaborate with local communities, destroying trust. (6) Opportunities to improve social / environmental justice and benefits for native/local communities forced to host green energy projects. Interview were then coded based on these overarching themes, extracting related text using Microsoft Excel. The information from this research allowed us to produce a narrative synthesis detailing areas of conflict and knowledge gaps and allowed us to create recommendations to improve collaboration between energy professionals and Native Hawaiians. This research has implications to guide potential green energy development in indigenous communities across the world. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract 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.

Abstract The growing demand for clean and sustainable energy sources has prompted the investigation of numerous renewable and ecologically friendly options. Among these, geothermal energy is particularly noteworthy because of its widespread availability, compact size, and consistent, weather-independent power production. A geothermal play fairway analysis (GPFA) model was created for the study area, which is located in Békés county, southeastern Hungary. The GPFA model approach in the current study is the first model developed in Hungary to achieve three main goals. firstly, to quantitatively assess the geothermal potential, secondly, to identify the most favorable areas for geothermal exploration and development, and thirdly, to evaluate the corresponding risk levels in the study area. The study focuses on identifying and assessing three main risk components associated with exploitable geothermal systems in the study area. The risk parameters consist of the heat source, reservoir fracture permeability, and seal. Advanced 3D seismic interpretation, geographic information system (GIS), and 3DHIP (heat in place) calculator techniques are used to evaluate subsurface structural and thermal models. Two phases of seismic interpretation are used; conventional interpretation phase focused on conventional seismic data interpretation and advanced attribute generation phase where various seismic attribute cube volumes are generated. Common Risk Segment Maps (CRS) for each risk parameter are created by combining data from all the elements contributing to that risk using GIS toolbox. The resulted CRS maps of the study area three risk parameters are summed to produce a Composite Common Risk Segment Map (CCRS) map. Based on the constructed CCRS map and the developed GPFA model, the study area holds valuable untapped geothermal potential, poses varying risk levels associated with geothermal exploration and development. The amount of risk resulting from the three risk components is not equal, and the reservoir fracture permeability is the main risk factor. The GPFA model is successfully narrowed down an expansive exploration area of around 350 km2 to just 4 highly promising targets with high geothermal favorability and low risk as future drilling targets. The constructed 3D thermal capacity model indicates that the average heat content in the study area is estimated to be 65,450 Petajoules per square kilometer (PJ/km2), with a recoverable heat energy of 6090 megawatt thermal per square kilometer (MWth/km2). The recoverable heat for the four selected targets is estimated under different production scenarios: a 30-year plan, a 20-year plan, and a 10-year plan and it ranges from 7.5 to 32 MWth/km2, 11 to 48 MWth/km2, 22.2 to 96.8 MWth/km2 respectively. The findings of this study have made important contributions to the field of geothermal exploration approaches and offer valuable insights for making well-informed decisions about sustainable energy development in the study area.

Abstract We analyze the relationship between geothermal energy production and seismic hazards in the Salton Sea Geothermal Field (SSGF) between 1972 and 2022. A clear increase in seismic activity accompanies geothermal energy production and is greatest to the east of the Brawley fault, where the amount of injection exceeds the amount of production. We estimate that, whereas there was a 2% chance of a M6.07 earthquake nucleating inside the SSGF within a fifty-year period prior to energy production (pre-production), there is an equal probability that a M6.70 earthquake nucleates there in a fifty-year period during which historically average energy production conditions persist (syn-production). Similarly, we estimate a 2% chance of a M7.15 and M6.92 occurring in the broader Brawley Seismic Zone (excluding the SSGF) within a fifty-year period during the pre- and syn-production eras, respectively. We find that linear regression models fail to reliably forecast the background seismicity rate as a function of fluid production and injection.

The editorial in "Frontiers in Energy Research" discusses the importance of integrating renewable energy for accelerated zero-carbon community transitions in urban areas. The research topic includes studies on adaptive energy management for airports, optimizing clean heating systems in diverse climates, hybrid energy solutions in developing economies, energy storage's impact on a green economy, the digital economy's role in low-carbon urban development, and integrated power flow design for EV charging stations. The studies emphasize the need for tailored energy management strategies, region-specific renewable solutions, and the critical role of flexible and integrated systems in achieving sustainable urban development. The editorial calls for further research on policy frameworks, financial incentives, cybersecurity, and the integration of renewable energy into urban transportation networks to accelerate zero-carbon transformations. [Extracted from the article]

• Numerical simulation for the action range of carbon dioxide blasting-induced cracking of HDR was conducted, in which the cooling effect of drilling fluid on the HDR reservoir close to the wellbore is taken into consideration. • The reservoir's material properties in the damaged section were established using fitting functions with temperature difference. Additionally, the temperature field of the reservoir in the affected region was established using temperature distribution functions. • The process of carbon dioxide blasting to stimulate HDR reservoir is modelled. The findings demonstrate that multiple stress concentrations occur during the carbon dioxide blasting process that causes HDR to crack. • The blasting crushing zone's scope is greatly influenced by the beginning temperature, but the fracture zone's scope is mostly unaffected. The extent of the crushing zone, which primarily determines the distribution of the blasting fracture zone, is not significantly impacted by the pressure plate's thickness. A cutting-edge technique for igniting hot dry rock (HDR) reservoirs is carbon dioxide blasting. The cooling effect of the drilling fluid was taken into consideration during a numerical simulation of the action range of carbon dioxide blasting-induced cracking. A temperature difference was used to determine the reservoir's material properties. Additionally, temperature distribution functions were used to create the temperature field in the reservoir. The blasting load is calculated using the pertinent theories and formulas of explosive blasting, and the process of blasting carbon dioxide to excite the HDR reservoir is modeled using COMSOL. The findings show that several stress concentrations take place during the blasting process. The fracture zone is created by the tensile stress concentration outside of the crushing zone, whereas the compressive stress concentration close to the blast hole creates the crushing zone. Furthermore, the effectiveness of carbon dioxide blasting fracturing would be affected by the beginning temperature and pressure plate thickness. Although the scope of the fracture zone is mostly unchanged, the initial temperature has a significant impact on the blasting crushing zone. The size of the crushing zone, which determines how the blasting fracture zone is distributed, is unaffected by the pressure plate's thickness. [ABSTRACT FROM AUTHOR]

• Pleasant experiences as heuristics for energy acceptance are presented. • High social acceptance of geothermal energy compared to hydro and nuclear. • Balneology correlates with perception of geothermal as safe and reliable. • Balneology correlates with perception of economic benefits of geothermal. • Risks of high social acceptance in context of heuristics are discussed. This research focuses on the question of how geothermal energy public perception is formed within a national context where news media do not pay attention to geothermal energy, and there is no active opposition to geothermal energy. The research builds on theoretical underpinnings of heuristics in energy perceptions and highlights the importance of physical experiences and associations as sources of experiential learning. Through a survey conducted among members of the general public in Slovenia, the role of thermal water recreation in shaping public perception of geothermal energy is empirically tested. The results show that, in this context, geothermal energy is highly positively assessed compared to hydro energy and nuclear energy, and that there is a correlation between frequency of thermal recreation and certain aspects of public perception of geothermal energy. The policy implications go beyond the simple recommendation of including tourism in public awareness campaigns but point to the specific vulnerability of energy acceptance in this context, where perceptions are formed based on "thinking fast", and where the media and the public discourse on geothermal energy have not (yet) considered the potential risks and opposition to geothermal projects. [ABSTRACT FROM AUTHOR]

The techno-economic evaluation of geothermal resources requires knowledge of the geofluid's thermophysical properties. While the properties of pure water and some specific brines have been studied extensively, no universally applicable model currently exists. This can result in a considerable degree of uncertainty as to how different geothermal resources will perform in practice. Geofluid modelling has historically been focused on two research fields: 1) partitioning the geofluid into separate phases, and 2) the estimation of the phases' thermophysical properties. Models for the two fields have commonly been developed separately. Recognising their potential synergy, we introduce GeoProp , a novel geofluid modelling framework, which addresses this application gap by coupling existing state-of-the-art fluid partitioning simulators, such as Reaktoro , with high-accuracy thermophysical fluid property computation engines, like CoolProp and ThermoFun. GeoProp has been validated against field experimental data as well as existing models for some incompressible binary fluids. We corroborate GeoProp's efficacy at modelling the thermophysical properties of geothermal geofluids via a case study on the heat content of different geofluids. Our results highlight the importance of accurately characterising the thermophysical properties of geofluids in order to quantify the resource potential and optimise the design of geothermal power plants. [ABSTRACT FROM AUTHOR]

The study of heat transfer in coal and rock masses is crucial for thermal storage engineering and geothermal utilization in abandoned mines. Nevertheless, previous research on heat transfer in this field has frequently neglected the interconnection between the flow and heat transfer of heat-working substances (HWS) within three-dimensional microcracks. To reveal the above mechanism, first, the multi-factor influence mechanism of coal and rock heat conduction was analyzed based on the gray system theory. Subsequently, meshes of the matrix and fractures were reconstructed using the digital core simulation technology based on scanning electron microscopy and computed tomography. Finally, a multi-dimensional and multi-scale coal conjugate heat transfer model was built. The results indicate that the heat flux of heat transfer in coal and rock in the heat inflow direction is 3.03–6.44 times as high as that in the direction perpendicular to the heat inflow direction. As the heat transfer of HWS continues, the influence of coal and rock composition diminishes, and the development degree of fractures becomes the major factor affecting heat transfer. Furthermore, alterations in phase state will result in modifications to the conjugated heat transfer mechanism. This study can provide guidance for HWS selection in abandoned mines. [ABSTRACT FROM AUTHOR]

The subway source heat pump system (SSHPS) incorporating a tunnel lining capillary heat exchanger (CHE) is an efficient technology for mitigating thermal degradation in subway tunnels while fully harnessing geothermal energy for heating and cooling purposes. Although extensive research has been conducted on the performance of tunnel lining heat exchangers, a practical engineering design method is still lacking. The objective of this study is to analyze the heat transfer characteristics of tunnel lining CHEs under typical design conditions and subsequently determine the optimal design conditions based on the analysis results. A numerical model of tunnel lining CHEs was developed using ANSYS, based on a demonstration project of SSHPS in Qingdao. Subsequently, the heat transfer performance of CHEs under various design parameters was simulated and analyzed. Finally, a statistical analysis was conducted to examine the impact of different factors on the heat transfer performance of CHEs. The analysis results indicate that the tunnel air temperature has the most significant influence, followed by the CHE inlet temperature parameter and the length of CHE laying pipe. The impact of the CHE inlet flow velocity is minimal. It is recommended that, during the design stage of a CHE heat exchanger, priority should be given to controlling the tunnel air temperature, optimizing the CHE inlet temperature and pipe length, and setting a minimum possible CHE inlet flow velocity. The optimal combination of design parameters for the tunnel lining CHEs were determined as follows: under the cooling mode, a tunnel air temperature of 23 °C, a CHE design inlet temperature of 39 °C, a laying pipe length of 6 m, and an inlet flow velocity of 0.08 m/s; under the heating mode, a tunnel air temperature of 20 °C, a design inlet temperature of 4 °C, a laying pipe length of 6 m, and an identical flow velocity of 0.08 m/s. This study can serve as a valuable reference for tunnel lining CHE design in practical engineering. [ABSTRACT FROM AUTHOR]

The utilization of ground-embedded structures for harnessing geothermal energy in space heating and cooling has gained significant attention over the past two decades. In this context, this paper reviews the use of tunnels, commonly known as energy tunnels, for exploiting shallow geothermal energy, providing an overview of the current status and future prospects. The different tunnel construction methods and their respective thermal activation processes are described. The factors influencing thermal performance and mechanical responses are analysed, alongside the economic and environmental benefits. This review confirms the viability of energy tunnels as a sustainable and renewable solution, as several studies have demonstrated their potential in providing a substantial amount of thermal energy and reducing natural gas consumption and significantly cutting CO 2 emissions. Energy tunnels can also potentially provide a reliable solution for road de-icing or enable efficient thermal energy storage when groundwater flow is negligible. Finally, current, and future innovations aimed at maximizing shallow geothermal exploitation are discussed, with a call for further research to explore additional thermal retrofitting techniques for the existing tunnels, innovative additions for lining concrete, fluid and pipe materials and integrating energy tunnels with other renewable energy sources in district heating networks. [ABSTRACT FROM AUTHOR]

Thermophilic microbial communities growing in low-oxygen environments often contain early-evolved archaea and bacteria, which hold clues regarding mechanisms of cellular respiration relevant to early life. Here, we conducted replicate metagenomic, metatranscriptomic, microscopic, and geochemical analyses on two hyperthermophilic (82-84 °C) filamentous microbial communities (Conch and Octopus Springs, Yellowstone National Park, WY) to understand the role of oxygen, sulfur, and arsenic in energy conservation and community composition. We report that hyperthermophiles within the Aquificota (Thermocrinis), Pyropristinus (Caldipriscus), and Thermoproteota (Pyrobaculum) are abundant in both communities; however, higher oxygen results in a greater diversity of aerobic heterotrophs. Metatranscriptomics revealed major shifts in respiratory pathways of keystone chemolithotrophs due to differences in oxygen versus sulfide. Specifically, early-evolved hyperthermophiles express high levels of high-affinity cytochrome bd and CydAA' oxidases in suboxic sulfidic environments and low-affinity heme Cu oxidases under microaerobic conditions. These energy-conservation mechanisms using cytochrome oxidases in high-temperature, low-oxygen habitats likely played a crucial role in the early evolution of microbial life., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)