To enable reliable exploration strategies for geothermal energy that have inherently lower economic and technical risks and hence increase public support, the multi-national, multi-disciplinary, and publicly funded FindHeat project is developing a novel, conceptual model-based geothermal exploration workflow. This workflow specifically focuses on faster turnaround times for exploration and appraisal of geothermal resources, making better use of legacy data and non-invasive geophysical techniques, and constraining uncertainties with respect to the size of the heat source and the range of possible heat production rates. Comprehensive social science research complements the technical work to set the foundation for new communication strategies that allow geothermal operators to earn the public trust that improved geothermal exploration and appraisal will lead to a more efficient and sustainable exploitation of geothermal energy. The workflow is being tested and validated at eight geologically diverse geothermal plays situated in Iceland, France, UK, Spain, and Netherlands, which allows us to demonstrate its economic and technical benefits as well as its societal impact.

High technical and economic risks stemming from the lack of detailed knowledge of the subsurface hold back large-scale investments in geothermal energy. In a survey conducted on nine use cases from diverse geological settings across Europe and with different purposes (electricity/heating and cooling) and project objectives (scientific/commercial), we identify the “common practice” and the aspiration for the “state of the art” in geothermal exploration. For each use case, the survey investigates what workflows have been adopted and what data acquired by which methods at different stages of exploration. This provided a benchmark for exploration in a range geothermal play types. The survey shows that this industry-standard base-case can be adapted to improve exploration success and efficiency by (1) applying numerical modelling in early stages of exploration to guide strategic data collection, (2) novel application of innovative technologies and (3) closer integration of software tools for static geological interpretation and dynamic heat flow simulation.

Direct Use Geothermal Systems (DUGS) are rapidly and densely deployed to meet the growing demand for renewable energy with less carbon emissions globally. The simulation of DUGS can provide a reservoir-scale understanding of geothermal resource assessment, where the geothermal system's lifetime and the injection well Bottom Hole Pressure (BHP) are used as performance indicators. However, there are inherent errors from numerical simulations of any engineering problems, due to approximating continuous partial differential equations by their discretized approximation in time and space. In this work, we establish an optimal numerical setup with reduced errors across the homogeneous, stratified and heterogeneous models for the simulation of a geothermal system. Next, we develop a standardized method for calculating recoverable Heat In Place (HIP) and an analytical solution for evaluating the HIP recovery factor across various geological models using a single forward simulation. We present reference examples on the design of DUGS simulations using the open-source software Delft Advanced Research Terra Simulator (open-DARTS). The open-DARTS platform enables accurate and efficient sensitivity and uncertainty analysis. Using Distance-Based Generalized Sensitivity Analysis (DGSA), we identify reservoir depth and discharge rate as the most influential parameters for geothermal projects across all three types of geological models.

The Netherlands aims to be CO2 neutral by 2050, aligning with the Paris Agreement. To achieve this, it is crucial to increase the contribution of geothermal energy to renewable energy sources, necessitating large-scale exploitation to speed up the energy transition. Only small-scale (1–2 km) geothermal field developments exist in the Netherlands primarily for heating. Expanding to extensive geothermal fields (10 km length) requires a strategic approach to well placement and consideration of the economic constraints associated with geothermal projects. The heterogeneity of the subsurface is a critical factor in developing large-scale geothermal reservoirs. This study introduces an innovative approach to optimising well placement based on geological trends, using a well-density function as a proof of concept. Implementing and optimising flexible well patterns for large-scale geothermal developments significantly enhances profitability compared to conventional oil and gas industry methods. Optimised flexible well patterns favour a long-term utilisation of energy recovered, minimise pressure extrema in the reservoir, and improve sweep efficiency. However, their application depends on reservoir operational decisions. The optimisation process ensures economic viability, even with lower heat prices. Broadly, this methodology could be key to scaling up geothermal developments to meet the objectives of the Paris Agreement.

Following publication of the original article, the author reported that the citations are not in brackets throughout the article. The citation currently reads: IEA (2023), Cozzi et al. (2020)., Hermans et al. (2018) Bloemendal and Hartog (2018) The citation should read: (IEA 2023), (Cozzi et al. 2020), (Hermans et al. 2018) (Bloemendal and Hartog 2018). The original article (Geerts et al. 2025) has been corrected.

This study investigates the thermal performance of closed-loop advanced geothermal systems under the influence of groundwater flow in deep sedimentary formations. By integrating advective heat transport into a 3D numerical model, we evaluate the combined effects of groundwater flow in deep sedimentary aquifers and geothermal heat transport and extraction using U-shaped closed-loop geothermal wells. The model is developed to simulate heat-transfer dynamics, incorporating well design with realistic casing and cement layers, layered geology with associated petrophysical uncertainties, and varying operational conditions. As study area, we selected the Midyan basin in Saudi Arabia, characterized by thick sedimentary formations and an elevated geothermal gradient. The results show that the advective heat transfer, induced by groundwater flow, significantly enhances system efficiency. Improvement in thermal power output increases by up to 27% over a 40-year operational period compared to conduction-only scenarios, particularly if groundwater flow is perpendicular to the lateral section of the wellbore. Sensitivity analysis reveals that geothermal gradient and reservoir depth are the most impactful geological parameters. Operational parameters such as injection rates (10—100 kg/s) and injection temperatures (25—45 °C) can be adjusted to further optimize the system performance, with 30 kg/s identified as the optimal injection rate that balances energy extraction and parasitic pumping losses. Well-design parameters, including diameters (0.114–0.245 m) and lateral length (0.5–3 km), also play a critical role, with longer lateral sections and larger diameters increasing the overall power output. These findings show the potential of U-shaped closed-loop advanced geothermal systems in sedimentary basins with dynamic groundwater flow and provide insights for optimizing geothermal energy systems in similar geological settings.

High-Temperature Aquifer Thermal Energy Storage (HT-ATES) has the potential to significantly increase the renewable heat share in heating systems. However, HT-ATES has not been implemented in the current energy system models because the widely applied numerical models for HT-ATES are computationally expensive. This leads to a lack of HT-ATES assessment from an energy system perspective. Therefore, an accurate and computationally efficient model that is widely applicable is needed to facilitate such implementation. This research aimed to develop a novel data-driven model that generates the temperature profile of an HT-ATES accurately and computationally efficiently. A trained machine learning algorithm predicts the recovery efficiency for an HT-ATES system, which, combined with other parameters, enables a nearest neighbor search to identify a suitable temperature profile. As a result, the temperature profile generated by the data-driven model has a root mean square error of 1.22 °C compared to the numerical model output. This error was shown to be larger for lower recovery efficiency values compared to higher values. The machine learning algorithm used to predict the recovery efficiency has a root mean square error of 1.45 percentage points. The data-driven model has a computation time of less than half a second, which is more than 180,000 times faster than the numerical model that was used to generate the data. This model is, therefore, suitable for integration in larger energy system models.

Energy storage plays a crucial role in decarbonizing the global energy system, particularly in the heating sector, which accounts for nearly 50 % of global energy demand. However, a significant challenge remains in balancing supply and demand from renewable energy sources. High-Temperature Aquifer Thermal Energy Storage (HT-ATES) presents a promising solution by enabling seasonal energy storage and shifting thermal loads efficiently. The successful implementation of HT-ATES requires a comprehensive understanding of both subsurface geological conditions and surface constraints to identify optimal storage sites. This study introduces a favorability assessment framework for HT-ATES systems across the Swiss Molasse Plateau (SMP), utilizing spatial multi-criteria play-based analysis (SMCPBA). Two key geological targets—the Cenozoic Molasse and Upper Mesozoic formations—are assessed alongside energy system criteria to pinpoint high-potential areas for future development. The findings highlight major urban centers such as Geneva, Lausanne, and Zurich as prime candidates due to their significant heat demand. However, broad-scale estimations necessitate higher-resolution data and site-specific feasibility studies for accurate assessment and implementation. The scalability of this methodology makes it applicable to various geographic contexts, supporting targeted pilot projects and feasibility assessments. Advancing HT-ATES technologies through refined methodologies and practical applications will contribute to Switzerland’s sustainable energy transition and long-term energy resilience.

Abstract High-Temperature Aquifer Thermal Energy Storage (HT-ATES) can be used to reduce greenhouse gas emissions from heating. The thermal recovery efficiency is the main parameter indicating the performance of an HT-ATES system and it is influenced by multiple aquifer properties and storage characteristics. This study presents a method for estimating recovery efficiency through numerical modeling, data analysis, and curve fitting. This method shows the relation between the recovery efficiency and various storage conditions, such as aquifer properties and storage temperature. In addition, this research explores an analytical relationship between energetic efficiency and recovery efficiency and verifies that relationship with the generated data. The proposed method can be used for the purpose of initial screening to estimate the performance of an HT-ATES system and for efficiently using HT-ATES as a component in larger energy system models. This method uses the modified Rayleigh number in combination with aquifer thickness and injected volume and has a R^2 of 85%. The analytical relation between energetic efficiency and recovery efficiency was shown to be accurate for all calculated energetic efficiency values above 60% and is less accurate with lower calculated energetic efficiency values.

The energy transition requires a reduction of CO2 emissions from anthropogenic activity. In Europe, heat amounts to 50% of the total gross energy consumption. In conduction-dominated geological settings, geothermal resources can supply renewable, baseload heat for direct uses, in almost all parts of Europe. In these settings, economic viability can be challenging and should be considered as coupled to the development of geothermal fields. The aim of this paper is to outline the way towards comprehensive uncertainty quantification. A methodology is proposed identifying three main categories of uncertainty sources to be evaluated based on three key performance indicators in a systematic approach. The sources of uncertainty include: a) subsurface characterization, b) development options and c) economics. The performance indicators are : i) cumulative energy generated, ii) system lifetime and iii) economic output. Each of the uncertainty sources and performance indicators are used to demonstrate the importance of a comprehensive approach to uncertainty quantification. Standardization and comprehensive analysis considering the combined uncertainty across the different uncertainty levels and over the key performance indicators is needed to enable more reliable and robust predictions of geothermal developments.

A geothermal doublet has been installed in a sedimentary reservoir for direct-use heating on the TU Delft campus, targeted to supply around 25 MW of thermal energy at peak conditions. This contribution presents the implementation and initial data collection from the doublet, including an initial evaluation of the logging and coring campaign. Nearly half of Netherlands natural gas consumption is allocated to heating, and the on-campus CO2 emissions from heating exceed 50%. The doublet has been designed with two primary aims of research and commercial heat supply, with the wells being completed in December 2023. The project will be operated by a commercial entity, and built into a larger thermal energy system including a high temperature underground storage system, with the first energy production planned in 2025. The research questions relate to field-scale geothermal operations, e.g. how reliable is the long-term energy production?, how do materials perform in the long-term? and how can geothermal projects be best monitored? The research programme involves the installation of a wide range of instruments alongside an extensive logging and coring program and monitoring network. The doublet has been cored, with substantial continuous samples from the heterogenous reservoir, alongside a large suite of open hole well logs in the reservoir and through casing logs in overlying geological units. A fiber-optic cable will monitor distributed pressure throughout the producer reservoir section, at approximately 2300m depth, which will be installed during commissioning. A local seismic monitoring network has been installed in the surrounding area with the aim of monitoring very low-magnitude natural or induced seismicity. The project is a key national research infrastructure and is being incorporated into the European EPOS (European Plate Observing System, https://www.epos-eu.org/), such that accessibility and data availability will be as wide as possible. All observations will be included in a digital-twin framework that will allow to make better decisions in future geothermal projects.

A geothermal well doublet, designed with two primary aims; one of research and the second of commercial thermal energy supply, is currently being installed on the campus of Delft University of Technology, with the wells being drilled in the second half of 2023. The project includes a comprehensive research program, involving the installation of a wide range of instruments alongside an extensive logging and coring program and monitoring network. The doublet has been cored, with continuous samples from the heterogenous reservoir being complimented with more distributed side-wall cores, alongside a large suite of open-hole well logs in the reservoir section of both wells. Such investigation is rarely undertaken in geothermal projects. A fiber optic cable will monitor the production well, and will be installed all-the-way down to the reservoir section when the well completion is installed, at approximately 2300m depth. The reservoir is the fluvial Lower Cretaceous Delft Sandstone that is used as a geothermal reservoir in a series of existing and planned doublets in the West Netherlands Basin. A local seismic monitoring network has been installed in the surrounding area with the aim of monitoring very low-magnitude natural or induced seismicity. A vertical observation well with electromagnetic sensors will be drilled in a few y ears’ time between the injector and producer to monitor cold-front propagation. The total project is targeted to supply around 25 MW of thermal energy at peak conditions, next to this project a thermal energy storage system is planned to provide a seasonal buffer. The project is a key national research infrastructure and is being incorporated into the European infrastructure EPOS (European Plate Observing System, https://www.epos-eu.org/), such that accessibility and data availability will be as wide as possible. All observations will be included in a digital-twin framework that will allow better decisions to be made in future geothermal projects. This paper presents the implementation and initial data collection from the project, including an initial evaluation of the logging and coring campaigns.

Nearly half of the Netherlands’ natural gas consump tion is allocated to heating, with direct -use geothermal heating being one of the available low-carbon energy solutions. A geothermal well doublet, designed with the two primary aims of research and commercial heat supply, is currently being installed on the campus of Delft University of Technology. The project is a key national research infrastructure and is being incorporated into the European sustainable and distributed infrastructure (EPOS: European Plate Observing System, https://www.epos-eu.org/), such that accessibility and data availability will be as wide as possible. All observations will be included in a digital-twin framework, which will allow us to make better decisions in future geothermal projects. The project includes a comprehens ive research program, involving the installation of a wide range of instruments alongside an extensive logging and coring program and monitoring network. The doublet has been cored, with substantial continuous samples from the heterogeneous reservoir, alongside a large suite of well logs in both the reservoir and overlying geological units. Such investigation is rarely undertaken in geothermal projects. A fiber-optic cable will monitor the producer well all the way down to the reservoir section, at approximately 2300m depth, in the Lower Cretaceous Delft Sandstone that is used as a geothermal reservoir in a series of existing and planned doublets in the West Netherlands Basin. A local seismic monitoring network has been installed in the surrounding area with the aim of monitoring very low-magnitude natural or induced seismicity. A vertical observation well with electromagnetic sensors will be drilled in the near future between the injector and producer to monitor cold-front propagation. This paper presents the initial modeling for the project and steps towards the production of a digital twin. Two modeling examples in the paper will emp hasize current operational challenges relevant to the project.

Geothermal energy extraction through deep mine systems offers the potential to reduce the cost of geothermal systems while meeting the cooling needs of deep mines. However, the injection of cold water into the subsurface triggers strongly coupled thermo-hydro-mechanical (THM) processes that can affect the stability of underground excavations. This study evaluates the impact of geothermal energy extraction on the temperature and stability of a deep mine. By quantifying the sensitivity of the mine temperature and stability to various parameters, we propose a scheme to optimize geothermal energy production, while achieving rapid mine cooling and maintaining stability. We first evaluate the impact of geothermal operations on mine temperature and stability through THM numerical modeling. The simulations show that poro-elastic stress quickly affects mine stability, while thermal stress has a more significant impact on the long-term stability. We then use Distance-based Generalized Sensitivity Analysis (DGSA) to quantify parameter sensitivity. The analysis identifies the distance between the mine system and the geothermal system as the most influential factor. Other important parameters include the injection rate, injection temperature, well spacing, coefficient of thermal expansion, permeability, Young's modulus, and heat capacity. Finally, we propose a DGSA-based optimization framework that accounts for subsurface uncertainty and validate the optimized results. Our results indicate that, with favorable geological conditions, a rational selection of system design parameters can enhance geothermal energy production while ensuring rapid mine cooling and stability. This study provides essential insights for the optimization of deep mine geothermal systems and supports effective decision-making.

Utilizing existing deep mining systems for geothermal extraction not only facilitates the development of geothermal systems but also helps meeting the cooling requirements for deep mining operations. In this study, a thermo-hydro-mechanical model of geothermal extraction in deep mines is developed to investigate the evolution of mine galleries stability and temperature, and the temperature changes in geothermal production wells. The uncertainty in system responses is predicted through the Bayesian Evidential Learning framework. Due to our limited understanding of the material properties and the scarcity of measurement data, uncertainties emerge in the forward simulations. Ideally, a comprehensive uncertainty analysis would be conducted to predict all possible outcomes and assess any risks. However, In light of the intractability of performing comprehensive uncertainty analyses in scenarios with vast unknown data, particularly due to the computational overhead of multiple inverse problemsolving, we employ the Bayesian Evidential Learning framework, which provides a feasible and rapid alternative for approximating prediction post-distributions and choosing the most informative data sets. Before implementing BEL, we employed Latin Hypercube Sampling to create 500 sets of realizations for forward simulations, and subsequently utilized global sensitivity analysis to evaluate the data's informational value, aiming to diminish the uncertainty in predictions. In this paper, the BEL framework is utilized to achieve two: firstly, to stochastically predict the responses of the system (stability and temperature) within the BEL framework, using machine learning to discover direct correlations between predictors (sensitive parameters) and targets (system responses). Subsequently, newly collected data can be utilized to predict the approximate posterior distributions of the corresponding gallery stability, temperature, and production well temperature, thus circumventing traditional data inversion steps. This framework can be adjusted to accommodate any predictions related to subsurface conditions; hence, our second goal involves predicting the system's long-term responses within the BEL based on shortterm data collection, forecasting posterior distributions from the acquired short-term data, and validating the efficacy of this approach. Our study indicates that in practical engineering, by (1) obtaining data of material properties and (2) key responses of short-term simulation, it is possible to predict the critical responses of the system in long-term geothermal extraction, thereby maximizing the information content of any measurement data while minimizing budget constraints and computational costs.

The subsurface provides multiple resources, the exploitation of which has a lasting impact on future potential provision. Establishing sustainability in terms of fundamental principles, and fitting these principles into a practical framework, is an ongoing endeavour focused mainly on surface activities. The principles of ecological economics lead to six challenges that summarize the current limitations of implementing science-based sustainable management of geological resources in the medium to deep subsurface: integrating value pluralism, defining sustainable scale, evaluating interferences in the subsurface, guaranteeing environmental justice, optimizing environmental and economic efficiency and handling uncertainties. Assessing and managing geological reservoirs is particularly challenging because of slow resource regeneration, complex spatial and temporal inter-actions, concealment and naturally dictated opportunities. In answer to the challenges, visions are proposed that outline how an indicator framework is needed for guidance, how indicators require reservoir models with extended spatial and temporal scope, how differences in social values in relation to the environment are to be considered and how real option games combined with life cycle assessment can be used for optimizing effi-ciency. These individual solutions relate to different facets of the same problem, and can be integrated into one overarching solution that takes the form of dynamic multi-criteria decision analysis.

With the increasing demand for mineral and alternative energy resources, as well as the gradual depletion of shallow resources, the exploitation and utilization of mineral resources and geothermal energy in deep strata is an effective way to solve the problem of resource shortage [1]. In recent years, as a new type of resource mining mode, the co-mining of deep mineral and geothermal energy has developed rapidly [2, 3]. This method can make use of the original equipment of the mine for geothermal exploitation. However, the deep co-mining system faces two significant challenges: the first is the significant uncertainty inherent in subsurface properties, while the second is the high levels of geostress and temperature associated with deep mining. These challenges are adding some constraints on the practicality of exploiting such systems and limit the feasibility of deep resource co-mining, so that modelling efforts are needed for actual risk assessment.
Consequently, we developed a Thermo-Hydro-Mechanical (THM) coupling framework for geothermal energy exploitation in deep mines using COMSOL to quantitatively characterize the temperature field of the geothermal system and predict the stress field of the mining system, considering the joint effects of large uncertainties and THM coupling. Through SGeMS, the uncertainty and spatial heterogeneity distribution of porosity are first generated. Then, the uncertainty of the hydraulic parameter [4] (permeability), mechanical parameter [5] (elastic modulus), and thermal parameter [6] (heat capacity and heat conductivity) was derived from the porosity. 500 samples were generated within a given uncertainty range, by means of Monte Carlo simulations. The spatial and temporal distributions of the temperature field of the geothermal system, and the stress field of the mining system were simulated, for each sample with COMSOL. Using the distance-based global sensitivity analysis [6], the most sensitive parameters for deep mining are identified, the heat storage capacity of the system and evolution of the maximum stress ratio are evaluated, including uncertainty.

The efficient operation and management of a geothermal project can be largely affected by geological, physical, operational and economic uncertainties. Systematic uncertainty quantification (UQ) involving these parameters helps to determine the probability of the focused outputs, e.g., energy production, Net Present Value (NPV), etc. However, how to efficiently assess the specific impacts of different uncertain parameters on the outputs of a geothermal project is still not clear. In this study, we performed a comprehensive UQ to a low-enthalpy geothermal reservoir using the GPU implementation of the Delft Advanced Research Terra Simulator (DARTS) framework with stochastic Monte Carlo samplings of uncertain parameters. With processing the simulation results, large uncertainties have been found in the production temperature, pressure drop, produced energy and NPV. It is also clear from the analysis that salinity influences the producing energy and NPV via changing the amount of energy carried in the fluid. Our work shows that the uncertainty in NPV is much larger than that in produced energy, as more uncertain factors were encompassed in NPV evaluation. An attempt to substitute original 3D models with upscaled 2D models in UQ demonstrates significant differences in the stochastic response of these two approaches in representation of realistic heterogeneity. The GPU version of DARTS significantly improved the simulation performance, which guarantees the full set (10,000 times) UQ with a large model (circa 3.2 million cells) finished within a day. With this study, the importance of UQ to geothermal field development is comprehensively addressed. This work provides a framework for assessing the impacts of uncertain parameters on the concerning system output of a geothermal project and will facilitate analyses with similar procedures.

Focus is on comparing stochastic, process-based and deterministic methods for modelling heterogeneity in hydraulic properties of fluvial geothermal reservoirs. Models are considered a generalized representation of a fluvial sequence in the upper part of the Gassum Formation in northern Denmark. Two ensemble realizations of process-based and stochastic heterogeneity were simulated using the state-of-the-art numerical modelling software, Delft Advanced Research Terra Simulator (DARTS), to assess differences on a statistically relevant sample size. Simulator settings were optimized to achieve two orders of magnitude improvement in simulation time. Our general findings show that the stochastic and process-based methods produce nearly identical results in terms of predicted breakthrough time and production temperature decline for high net-to-gross ratios (N/G). Simple homogenous and layered models overestimate breakthrough and underestimate temperature decline. More complex representation of facies in process-based models show smaller variance in results and stay within the limits of ensemble runs compared to simpler facies representation. Results indicate that a multivariate Gaussian based stochastic representation of heterogeneity provides comparable thermal response to a process-based model in fluvial systems of similar quality.

The Kingdom of Saudi Arabia (KSA) has vast geothermal energy resources. When developed, these markedly strengthen the country's goals of achieving a carbon-neutral economy. To demonstrate the feasibility and techno-economic performance of small-scale, hydrothermal well doublet systems for direct use in KSA, we perform reservoir and wellbore flow and heat-transport simulations as well as an economic analysis. The maximum permissible flowrate is constrained to avoid thermoelastic fracturing in the near-wellbore region. Reservoir conditions of a sedimentary basin along the Red Sea coast (near Al Wajh) provide an ideal study case to which we add economic parameters considered representative for KSA. We derive a Levelized Cost of Heat (LCOH) ranging from 49 to 128 $/MWh for 50-mD hydrothermal doublet systems with an optimal well spacing of 600 m and a flowrate ranging from 110 kg/s to 50 kg/s. LCOH is strongly influenced by decreasing reservoir transmissivity. Also, a minimum injection temperature is required to avoid thermoelastic fracturing. Our economic analysis further highlights that capacity factor and well-drilling cost have the greatest impact on LCOH. Thus, this study provides a guide and workflow to conduct techno-economic investigations for decision-making, risk mitigation, optimizing geothermal-energy-extraction and economic-performance conditions of hydrothermal doublet systems.