Low-enthalpy geothermal heat production is becoming increasingly common, which leads to the potentially competitive use of the available subsurface space, especially in densely populated urban areas. A specific challenge presented by the high density of different geothermal systems is understanding the details of convective and conductive heat flow processes and detailed monitoring of properties and processes in the subsurface.

On the TU Delft campus, we aim to drill a borehole of around 4.5 km depth to be used for the exploration, observation, and monitoring of subsurface processes that will be part of a larger research infrastructure under development. This so-called urban energy laboratory includes – in addition to the deep multi-use borehole – a well-instrumented geothermal doublet drilled in 2023, reaching to a depth of 2.2 km; a local seismic monitoring system (installed in 2022); an ultra-sensitive portable seismic monitoring array; and a high-temperature aquifer heat storage system (HT-ATES), for which a pilot well was drilled in 2024. With this urban energy laboratory, we want to tackle problems and better understand processes related to multiple and/or competing subsurface uses in urban environments. The deep exploration and monitoring borehole is designed specifically to monitor fluid and/or flux movement in 3D with unprecedented precision, aiming to understand the propagation of the geothermal cold front and reservoir pressures.

During the 3 d International Continental Scientific Drilling Program (ICDP)-sponsored UrbEnLab workshop, 75 scientists from 17 countries met in Delft, the Netherlands, in June 2024 to prioritize the scientific ambitions of the deep exploration and monitoring borehole and to discuss potential techniques that could be applied to tackle them. Assessing the life cycle of a geothermal system situated in a complex heterogeneous sedimentary system was defined as the broad aim, with revealing the detailed flow field established being a key priority.

Integrating renewable energy into district heating creates a heat supply–demand mismatch that High-Temperature Aquifer Thermal Energy Storage (HT-ATES) can help address. However, the potential greenhouse gas emission reduction and financial benefits of HT-ATES have received limited attention. Additionally, the interplay between the demand, supply components, and HT-ATES has been overlooked, while the assessment of integrating HT-ATES into a district heating system is crucial to understanding the benefits of the HT-ATES implementation. This study evaluates the integration of HT-ATES into a district heating system, focusing on both economic and environmental performance indicators. It novelly accounts for the dynamic operational interactions between HT-ATES and other system components, enabling a more realistic assessment of operational choices. The model is applied to a case study of a simplified district heating system. The results show that the relative size of the heat supplier compared to heat demand is a key determinant of the cost-effectiveness of HT-ATES. In the case study, a geothermal doublet reduced the levelized cost of heat by 25–37 €/MWh compared to a gas boiler, while also reducing reliance on fossil fuels. In contrast, HT-ATES had a limited impact on total system costs, regardless of whether it operated when stored heat was available or was used for peak shaving. Nevertheless, HT-ATES increased the renewable energy share by 9%–18% across all scenarios. Furthermore, the optimal geothermal capacity differed depending on whether HT-ATES was included. Finally, while a high renewable energy share can be cost-effective, achieving 100% renewable heat was found to be highly cost-ineffective in this case. These results support informed decision-making on HT-ATES implementation under appropriate system design conditions.

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.

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 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.

Characterising fractures in geothermal reservoirs is crucial for understanding heat and fluid flow, as fractures control reservoir permeability. Due to data scarcity, estimating fracture network properties remains uncertain. Dynamic data, such as well tests, provides indirect insights into subsurface properties and workflows have been developed to illustrate how uncertainty in fracture data affects flow behaviour. However, they use simplified, randomly generated fracture geometries limiting their applicability to real-world scenarios. This study presents a machine learning workflow for characterizing fractured reservoirs using transient data, focusing on geothermal reservoirs. A comprehensive dataset of 5000 geologically consistent Discrete Fracture Networks (DFNs) was generated using GeoDFN and directly linked to MRST for simulations. The workflow then applies a k-medoids clustering approach, using dynamic time warping (DTW) as a distance metric, to cluster pressure responses with similar transient behaviour. We identified 18 distinct pressure behaviour. Linking clusters to fracture properties reveals that fracture intensity, aperture, and length have the most significant impact on pressure behaviour, while fracture set type was found to be the least important factor. Future work will extend this workflow to temperature transient data and apply advanced machine learning techniques for both forward and inverse modelling of fractured geothermal reservoirs.

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-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.

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.

Fractures are ubiquitous in geological formations and can often have an impact on subsurface applications such as geothermal energy, groundwater management or CO2 storage. Quantifying the relationship between the uncertainties inherent to fracture networks and the corresponding flow behaviour for these applications remains an open challenge. Simulation studies that are based on outcrop analogues of fracture networks have yielded many new insights about heat and mass transfer in fractured geological formations but are restricted to a limited number of fracture network realizations, simplified assumptions about fracture network properties or deterministic models, making it difficult to analyse a wide range of uncertainties. This study introduces a flexible workflow that generates ensembles of geologically plausible fracture networks that can be based on statistical data from outcrop analogues. The fracture networks are generated using a computationally efficient approach that combines mechanical and statistical methods. The ensembles are then seamlessly linked to multi-purpose flow and transport simulations where the fractures are represented explicitly in a porous and permeable rock matrix. This approach can enable new uncertainty quantification methods, supported by machine-learning-based emulators, to analyse how fracture network properties, such as fracture intensity, fracture aperture or fracture orientation, influence heat and mass transfer in fractured geological formations. The workflow is illustrated using two classic example applications pertinent to fracture network modelling – one based on outcrop data to assess thermal behaviour in geothermal systems, and one synthetic study to analyse the transition from matrix-dominated to fracture-dominated flow – and released as open-source code.

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.

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.

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.

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.

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.

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.

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.

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.