Alexandros Daniilidis
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1
Sizing optimization of district heating components with High-Temperature Aquifer Thermal Energy Storage
Techno-economic analysis for different renewable energy levels
District heating systems must decarbonize by replacing fossil fuel-based heat sources with sustainable alternatives. To fully utilize the capacity of renewable sources, seasonal thermal energy storage is necessary due to seasonal supply–demand mismatches. High-Temperature Aquifer Thermal Energy Storage (HT-ATES) offers a promising solution, but its cost-effective deployment requires coordinated sizing with the sustainable heat source, which has received limited attention in literature. This study presents a techno-economic and renewable share analysis of district heating systems incorporating deep geothermal heat, solar thermal collectors, HT-ATES, and gas boilers. We identified representative heat demand profiles for different climates by clustering to ensure broader applicability of the findings. We show that the demand profile is important for the cost-effectiveness of district heating. The results show that HT-ATES is cost-effective in most scenarios compared to natural gas boilers, particularly when paired with a geothermal source. Geothermal energy was generally more economically favorable than solar thermal collectors. Achieving 100% renewable heat supply is cost-inefficient because it requires large additional capacity for limited additional load, increasing costs by 15% compared to 99% renewable share. However, 90% renewable share can be reached with only 5% cost increase compared to the optimum, using geothermal energy. These insights provide guidance for district heating designers, operators and policymakers on optimal component sizing and promote the informed use of HT-ATES to support cost-effective decarbonization of district heating. Representative demand profiles are expected to be used often in research, as they proved influential on the levelized cost of heat.
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District heating systems must decarbonize by replacing fossil fuel-based heat sources with sustainable alternatives. To fully utilize the capacity of renewable sources, seasonal thermal energy storage is necessary due to seasonal supply–demand mismatches. High-Temperature Aquifer Thermal Energy Storage (HT-ATES) offers a promising solution, but its cost-effective deployment requires coordinated sizing with the sustainable heat source, which has received limited attention in literature. This study presents a techno-economic and renewable share analysis of district heating systems incorporating deep geothermal heat, solar thermal collectors, HT-ATES, and gas boilers. We identified representative heat demand profiles for different climates by clustering to ensure broader applicability of the findings. We show that the demand profile is important for the cost-effectiveness of district heating. The results show that HT-ATES is cost-effective in most scenarios compared to natural gas boilers, particularly when paired with a geothermal source. Geothermal energy was generally more economically favorable than solar thermal collectors. Achieving 100% renewable heat supply is cost-inefficient because it requires large additional capacity for limited additional load, increasing costs by 15% compared to 99% renewable share. However, 90% renewable share can be reached with only 5% cost increase compared to the optimum, using geothermal energy. These insights provide guidance for district heating designers, operators and policymakers on optimal component sizing and promote the informed use of HT-ATES to support cost-effective decarbonization of district heating. Representative demand profiles are expected to be used often in research, as they proved influential on the levelized cost of heat.
A High Temperature Aquifer Thermal Energy Storage system will be installed on the TU Delft campus, The Netherlands, utilising an aquifer at 120-180 m depth to store hot water (>60 °C). For optimal recovery efficiency, it is essential that the injected water stays near the injector well until it can be extracted, and that there is no mixing with cold water. By monitoring the spread of injected water using Controlled-Source Electromagnetics, the heat transport in this subsurface storage system could be better understood. For this, a surface-to-borehole survey design was proposed, sensitive to the resistivity at the depth of the aquifer through the vertical component of the electric field. 3D time-lapse data will be acquired using receivers inside a monitoring borehole and horizontal electric dipole sources surrounding the site. Optimal source positions will be determined through a feasibility study, using forward-modelling and inversion of synthetic data. By relating the resistivity of the water to its temperature, this data can be used to image hot plume propagation over time. However, the relationship between conductivity and temperature is expected to vary between sites and for large temperature ranges. Laboratory measurements will therefore be performed, uncovering site-specific resistivity-temperature relationships to improve the interpretation of Controlled-Source Electromagnetic data. Lastly, a future research interest lies in the combination of monitoring data with thermodynamic modelling. Through workflows of data-assimilation or process-based inversion, these methods could complement each other, leading to an enhanced understanding of heat flow in the aquifer.
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A High Temperature Aquifer Thermal Energy Storage system will be installed on the TU Delft campus, The Netherlands, utilising an aquifer at 120-180 m depth to store hot water (>60 °C). For optimal recovery efficiency, it is essential that the injected water stays near the injector well until it can be extracted, and that there is no mixing with cold water. By monitoring the spread of injected water using Controlled-Source Electromagnetics, the heat transport in this subsurface storage system could be better understood. For this, a surface-to-borehole survey design was proposed, sensitive to the resistivity at the depth of the aquifer through the vertical component of the electric field. 3D time-lapse data will be acquired using receivers inside a monitoring borehole and horizontal electric dipole sources surrounding the site. Optimal source positions will be determined through a feasibility study, using forward-modelling and inversion of synthetic data. By relating the resistivity of the water to its temperature, this data can be used to image hot plume propagation over time. However, the relationship between conductivity and temperature is expected to vary between sites and for large temperature ranges. Laboratory measurements will therefore be performed, uncovering site-specific resistivity-temperature relationships to improve the interpretation of Controlled-Source Electromagnetic data. Lastly, a future research interest lies in the combination of monitoring data with thermodynamic modelling. Through workflows of data-assimilation or process-based inversion, these methods could complement each other, leading to an enhanced understanding of heat flow in the aquifer.
Journal article
(2026)
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David Bruhn, Hemmo A. Abels, Patrick Fulton, Virginie Harcouët-Menou Harcouët-Menou, Ernst Huenges, Stefan Jansen, Alexis Koulidis, Susanne Laumann, Haiyan Lei, Joseph Moore, Paula Rulff, Thorben Schöfisch, Auke Barnhoorn, Evert Slob, Philip J. Vardon, Liliana Vargas Meleza, Denis Voskov, Claire Bossennec, Aoife K. Braiden, Maren Brehme, Romain Chassagne, Alexandros Daniilidis, Mathieu Darnet, Guy Drijkoningen
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. ...
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. ...
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.
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.
Fluvial clastic sequence is the typical reservoir rock to develop the project of Direct Use Geothermal Systems (DUGS). However, limited and unknown subsurface data bring challenges to accurately characterize the geology of the fluvial depositional environment and to capture the details of the sand body distribution and connectivity. In this work, we utilize the process-based modeling (PBM) approach (Flumy) and object-based modeling approach (OBM) (Fluvsim) to develop a robust framework to evaluate the heterogeneity representation of the fluvial depositional environment. An ensemble of geological models with different global net-to-gross ratios (N/G) is created using PBM and OBM. The generated high-resolution models aim to cover a wide range of N/G from 30% to 80% which is typical for Dutch geothermal sediments. For the given models, a static analysis using the Lorenz coefficient shows a large range of variability in heterogeneity levels for both PBM and OBM models. This wide range of heterogeneity levels leads to a broad variation in thermal breakthrough time during 100 years of thermal production. We also find that the OBM and PBM converge to nearly the same distribution of predicted thermal breakthrough time when the overall N/G is 80%.
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Fluvial clastic sequence is the typical reservoir rock to develop the project of Direct Use Geothermal Systems (DUGS). However, limited and unknown subsurface data bring challenges to accurately characterize the geology of the fluvial depositional environment and to capture the details of the sand body distribution and connectivity. In this work, we utilize the process-based modeling (PBM) approach (Flumy) and object-based modeling approach (OBM) (Fluvsim) to develop a robust framework to evaluate the heterogeneity representation of the fluvial depositional environment. An ensemble of geological models with different global net-to-gross ratios (N/G) is created using PBM and OBM. The generated high-resolution models aim to cover a wide range of N/G from 30% to 80% which is typical for Dutch geothermal sediments. For the given models, a static analysis using the Lorenz coefficient shows a large range of variability in heterogeneity levels for both PBM and OBM models. This wide range of heterogeneity levels leads to a broad variation in thermal breakthrough time during 100 years of thermal production. We also find that the OBM and PBM converge to nearly the same distribution of predicted thermal breakthrough time when the overall N/G is 80%.
Preprint
(2026)
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Yuan Chen, Guillaume Rongier, James Robert Mullins, Denis Voskov, Alexandros Daniilidis
Low-enthalpy geothermal doublets for direct-use heating are highly sensitive to subsurface heterogeneity and operational uncertainty. This study quantifies these uncertainties for the Delft campus geothermal system using an integrated workflow that couples geological modeling with GPU-accelerated, high-spatial-resolution reservoir simulation. Ensembles of three-dimensional regional-scale facies models of the Delft Sandstone Member, with and without conditioning to Geothermie Delft (GTD) well data, were generated using object-based modeling and sequential indicator simulation. Porosity was modeled by sequential Gaussian simulation, and permeability was derived from a nuclear magnetic resonance–based porosity–permeability correlation calibrated to GTD well data, yielding higher permeability than core-based correlations for porosity below 15%. Lorenz coefficients indicate strong variability in property distributions, resulting in a wide spread of production temperatures. In total, 2000 geological realizations were simulated over 50 years using the GPU-enabled open-source Delft Advanced Research Terra Simulator (open-DARTS) under maximum and 2025-demand production schemes. Conditioning to GTD wells reduces uncertainty in production bottom-hole temperature (BHT) and pressure (BHP) while maintaining injection pressure below Dutch regulatory limits. SIS models exhibit greater temperature and pressure variability than OBM models due to lower sand-body continuity. Despite large 80% confidence intervals, P50 production temperatures remain comparable for conditioned models. Distance-based generalized sensitivity analysis identifies net-to-gross ratio and the porosity–permeability correlation as dominant controls on thermal response. The 2025-demand scheme delays cold-front propagation. Results demonstrate that ensemble-based, GPU-accelerated high-spatial-resolution simulations enable robust and efficient uncertainty quantification for direct-use geothermal systems, highlighting the importance of well conditioning and reservoir heterogeneity characterization in constraining thermal responses.
...
Low-enthalpy geothermal doublets for direct-use heating are highly sensitive to subsurface heterogeneity and operational uncertainty. This study quantifies these uncertainties for the Delft campus geothermal system using an integrated workflow that couples geological modeling with GPU-accelerated, high-spatial-resolution reservoir simulation. Ensembles of three-dimensional regional-scale facies models of the Delft Sandstone Member, with and without conditioning to Geothermie Delft (GTD) well data, were generated using object-based modeling and sequential indicator simulation. Porosity was modeled by sequential Gaussian simulation, and permeability was derived from a nuclear magnetic resonance–based porosity–permeability correlation calibrated to GTD well data, yielding higher permeability than core-based correlations for porosity below 15%. Lorenz coefficients indicate strong variability in property distributions, resulting in a wide spread of production temperatures. In total, 2000 geological realizations were simulated over 50 years using the GPU-enabled open-source Delft Advanced Research Terra Simulator (open-DARTS) under maximum and 2025-demand production schemes. Conditioning to GTD wells reduces uncertainty in production bottom-hole temperature (BHT) and pressure (BHP) while maintaining injection pressure below Dutch regulatory limits. SIS models exhibit greater temperature and pressure variability than OBM models due to lower sand-body continuity. Despite large 80% confidence intervals, P50 production temperatures remain comparable for conditioned models. Distance-based generalized sensitivity analysis identifies net-to-gross ratio and the porosity–permeability correlation as dominant controls on thermal response. The 2025-demand scheme delays cold-front propagation. Results demonstrate that ensemble-based, GPU-accelerated high-spatial-resolution simulations enable robust and efficient uncertainty quantification for direct-use geothermal systems, highlighting the importance of well conditioning and reservoir heterogeneity characterization in constraining thermal responses.
Journal article
(2026)
-
Yuan Chen, Guillaume Rongier, James Robert Mullins, Denis Voskov, Alexandros Daniilidis
Low-enthalpy geothermal doublets for direct-use heating are highly sensitive to subsurface heterogeneity and operational uncertainty. This study quantifies these uncertainties for the Delft campus geothermal system using an integrated workflow that couples geological modeling with GPU-accelerated, high-spatial-resolution reservoir simulation. Ensembles of three-dimensional regional-scale facies models of the Delft Sandstone Member, with and without conditioning to Geothermie Delft (GTD) well data, were generated using object-based modeling and sequential indicator simulation. Porosity was modeled by sequential Gaussian simulation, and permeability was derived from a nuclear magnetic resonance-based porosity–permeability correlation calibrated to GTD well data, yielding higher permeability than core-based correlations for porosity below 15%. Lorenz coefficients indicate strong variability in property distributions, resulting in a wide spread of production temperatures. In total, 2000 geological realizations were simulated over 50 years using the GPU-enabled open-source Delft Advanced Research Terra Simulator (open-DARTS) under maximum and 2025-demand production schemes. Conditioning to GTD wells adds considerable data worth by constraining uncertainty in production well bottom-hole temperature (BHT) and pressure (BHP), while keeping injection pressure within Dutch regulatory limits. SIS models exhibit greater temperature and pressure variability than OBM models due to lower sand-body continuity. Despite large 80% confidence intervals, P50 production temperatures remain comparable for conditioned models. Distance-based generalized sensitivity analysis identifies net-to-gross ratio and the porosity–permeability correlation as dominant controls on thermal response. The 2025-demand scheme delays cold-front propagation. Results demonstrate that ensemble-based, GPU-accelerated high-spatial-resolution simulations enable robust and efficient uncertainty quantification for direct-use geothermal systems, highlighting the importance of well conditioning and reservoir heterogeneity characterization in constraining thermal responses.
...
Low-enthalpy geothermal doublets for direct-use heating are highly sensitive to subsurface heterogeneity and operational uncertainty. This study quantifies these uncertainties for the Delft campus geothermal system using an integrated workflow that couples geological modeling with GPU-accelerated, high-spatial-resolution reservoir simulation. Ensembles of three-dimensional regional-scale facies models of the Delft Sandstone Member, with and without conditioning to Geothermie Delft (GTD) well data, were generated using object-based modeling and sequential indicator simulation. Porosity was modeled by sequential Gaussian simulation, and permeability was derived from a nuclear magnetic resonance-based porosity–permeability correlation calibrated to GTD well data, yielding higher permeability than core-based correlations for porosity below 15%. Lorenz coefficients indicate strong variability in property distributions, resulting in a wide spread of production temperatures. In total, 2000 geological realizations were simulated over 50 years using the GPU-enabled open-source Delft Advanced Research Terra Simulator (open-DARTS) under maximum and 2025-demand production schemes. Conditioning to GTD wells adds considerable data worth by constraining uncertainty in production well bottom-hole temperature (BHT) and pressure (BHP), while keeping injection pressure within Dutch regulatory limits. SIS models exhibit greater temperature and pressure variability than OBM models due to lower sand-body continuity. Despite large 80% confidence intervals, P50 production temperatures remain comparable for conditioned models. Distance-based generalized sensitivity analysis identifies net-to-gross ratio and the porosity–permeability correlation as dominant controls on thermal response. The 2025-demand scheme delays cold-front propagation. Results demonstrate that ensemble-based, GPU-accelerated high-spatial-resolution simulations enable robust and efficient uncertainty quantification for direct-use geothermal systems, highlighting the importance of well conditioning and reservoir heterogeneity characterization in constraining thermal responses.
Journal article
(2026)
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Elahe Kamel Targhi, Guillaume Rongier, Pierre Olivier Bruna, Alexandros Daniilidis, Sebastian Geiger
Naturally fractured reservoirs are essential for subsurface energy production and storage. However, the complexity and uncertainty inherent to fracture network properties make it difficult to characterise fluid flow within them. This study presents an unsupervised machine learning workflow that constrains uncertainty by establishing a systematic link between the pressure transient response observed at the well and the underlying fracture network properties. We generate a geologically consistent ensemble of 4,850 discrete fracture networks (DFNs) and simulate pressure transient responses for the same geometries under three matrix-fracture permeability configurations. For each dataset, we group pressure derivative responses into geologically interpretable flow behaviour clusters using Dynamic Time Warping (DTW) based K-medoids clustering. The resulting cluster medoids provide representative pressure derivative responses that summarise the dominant flow regime sequence within each class. The workflow consistently identifies four stable clusters across all datasets, each characterised by a distinct and repeatable sequence of diagnostic flow regimes consistent with a bounded range of fracture network properties. Feature importance ranking and SHAP values derived from a random forest classifier show that fracture intensity, wellbore fracture length, and backbone fracture fraction provide the strongest geological controls on cluster separation and hence on the emergent diagnostic signatures. Comparing clusters across datasets shows that 64.1% of DFNs retain their cluster membership, indicating that the clustering structure is primarily controlled by DFN geometry. However, for a fixed DFN, the pressure transient response varies across matrix-fracture permeability configurations, producing systematic shifts in derivative levels and in the dominance of specific flow regimes. The representative pressure derivative responses associated with each flow behaviour cluster are therefore not identical across datasets and must be interpreted within the matrix-fracture permeability configuration. Overall, the proposed framework provides a basis to constrain geological uncertainty and prioritise high-impact parameters for data acquisition in naturally fractured reservoirs, thereby improving reservoir characterisation and decision making in the early appraisal stage.
...
Naturally fractured reservoirs are essential for subsurface energy production and storage. However, the complexity and uncertainty inherent to fracture network properties make it difficult to characterise fluid flow within them. This study presents an unsupervised machine learning workflow that constrains uncertainty by establishing a systematic link between the pressure transient response observed at the well and the underlying fracture network properties. We generate a geologically consistent ensemble of 4,850 discrete fracture networks (DFNs) and simulate pressure transient responses for the same geometries under three matrix-fracture permeability configurations. For each dataset, we group pressure derivative responses into geologically interpretable flow behaviour clusters using Dynamic Time Warping (DTW) based K-medoids clustering. The resulting cluster medoids provide representative pressure derivative responses that summarise the dominant flow regime sequence within each class. The workflow consistently identifies four stable clusters across all datasets, each characterised by a distinct and repeatable sequence of diagnostic flow regimes consistent with a bounded range of fracture network properties. Feature importance ranking and SHAP values derived from a random forest classifier show that fracture intensity, wellbore fracture length, and backbone fracture fraction provide the strongest geological controls on cluster separation and hence on the emergent diagnostic signatures. Comparing clusters across datasets shows that 64.1% of DFNs retain their cluster membership, indicating that the clustering structure is primarily controlled by DFN geometry. However, for a fixed DFN, the pressure transient response varies across matrix-fracture permeability configurations, producing systematic shifts in derivative levels and in the dominance of specific flow regimes. The representative pressure derivative responses associated with each flow behaviour cluster are therefore not identical across datasets and must be interpreted within the matrix-fracture permeability configuration. Overall, the proposed framework provides a basis to constrain geological uncertainty and prioritise high-impact parameters for data acquisition in naturally fractured reservoirs, thereby improving reservoir characterisation and decision making in the early appraisal stage.
Geophysics education is often challenging, as it entails explaining complicated physical processes that take place inside the Earth. Because these processes happen below the surface of the Earth, it can be difficult for students to connect to the material and understand what is happening. As a result, it is hard for students to make the link between the abstract explanation of the processes to the physical measurements that are performed during fieldwork.
A novel way to close the gap between theory and fieldwork is the use of Virtual Reality or VR. VR allows a student to fully immerse themselves into a digital twin of reality and to experience and visualize processes that are invisible in real life. This is the purpose of Geoscience Processes Virtual Education or GeoProVE. In this application, we have developed a fully immersive and interactive scenario where a student can learn about Ground Penetrating Radar or GPR. The use performs a GPR measurement along a line and is guided with questions to understand how the data are acquired and why specific patterns arise. One of the major features is the ability to pull the subsurface out of the ground, to see how the waves propagate through the subsurface and interact with objects, such as pipes and the water table, in the subsurface. Several setups with increasing complexity are shown to the students, with a strong emphasis on challenge-based learning through a scoring system.
Aside from the GPR scenario, a scenario focused on offshore 3D seismics is also in development for GeoProVE, with the aim to create additional scenarios focused on ERT and geothermal applications. GeoProVE is intended to become fully open source so other developers can contribute to the knowledge base. The application has shown positive engagement from students for geophysics education. We will demonstrate the development of GeoProVE along with its main features. ...
A novel way to close the gap between theory and fieldwork is the use of Virtual Reality or VR. VR allows a student to fully immerse themselves into a digital twin of reality and to experience and visualize processes that are invisible in real life. This is the purpose of Geoscience Processes Virtual Education or GeoProVE. In this application, we have developed a fully immersive and interactive scenario where a student can learn about Ground Penetrating Radar or GPR. The use performs a GPR measurement along a line and is guided with questions to understand how the data are acquired and why specific patterns arise. One of the major features is the ability to pull the subsurface out of the ground, to see how the waves propagate through the subsurface and interact with objects, such as pipes and the water table, in the subsurface. Several setups with increasing complexity are shown to the students, with a strong emphasis on challenge-based learning through a scoring system.
Aside from the GPR scenario, a scenario focused on offshore 3D seismics is also in development for GeoProVE, with the aim to create additional scenarios focused on ERT and geothermal applications. GeoProVE is intended to become fully open source so other developers can contribute to the knowledge base. The application has shown positive engagement from students for geophysics education. We will demonstrate the development of GeoProVE along with its main features. ...
Geophysics education is often challenging, as it entails explaining complicated physical processes that take place inside the Earth. Because these processes happen below the surface of the Earth, it can be difficult for students to connect to the material and understand what is happening. As a result, it is hard for students to make the link between the abstract explanation of the processes to the physical measurements that are performed during fieldwork.
A novel way to close the gap between theory and fieldwork is the use of Virtual Reality or VR. VR allows a student to fully immerse themselves into a digital twin of reality and to experience and visualize processes that are invisible in real life. This is the purpose of Geoscience Processes Virtual Education or GeoProVE. In this application, we have developed a fully immersive and interactive scenario where a student can learn about Ground Penetrating Radar or GPR. The use performs a GPR measurement along a line and is guided with questions to understand how the data are acquired and why specific patterns arise. One of the major features is the ability to pull the subsurface out of the ground, to see how the waves propagate through the subsurface and interact with objects, such as pipes and the water table, in the subsurface. Several setups with increasing complexity are shown to the students, with a strong emphasis on challenge-based learning through a scoring system.
Aside from the GPR scenario, a scenario focused on offshore 3D seismics is also in development for GeoProVE, with the aim to create additional scenarios focused on ERT and geothermal applications. GeoProVE is intended to become fully open source so other developers can contribute to the knowledge base. The application has shown positive engagement from students for geophysics education. We will demonstrate the development of GeoProVE along with its main features.
A novel way to close the gap between theory and fieldwork is the use of Virtual Reality or VR. VR allows a student to fully immerse themselves into a digital twin of reality and to experience and visualize processes that are invisible in real life. This is the purpose of Geoscience Processes Virtual Education or GeoProVE. In this application, we have developed a fully immersive and interactive scenario where a student can learn about Ground Penetrating Radar or GPR. The use performs a GPR measurement along a line and is guided with questions to understand how the data are acquired and why specific patterns arise. One of the major features is the ability to pull the subsurface out of the ground, to see how the waves propagate through the subsurface and interact with objects, such as pipes and the water table, in the subsurface. Several setups with increasing complexity are shown to the students, with a strong emphasis on challenge-based learning through a scoring system.
Aside from the GPR scenario, a scenario focused on offshore 3D seismics is also in development for GeoProVE, with the aim to create additional scenarios focused on ERT and geothermal applications. GeoProVE is intended to become fully open source so other developers can contribute to the knowledge base. The application has shown positive engagement from students for geophysics education. We will demonstrate the development of GeoProVE along with its main features.
The authors regret that during a post-publication review of the numerical model, we identified that rock thermal conductivity and rock heat capacity were not transferred properly for the ensemble run and were set to zero for the energy conservation equation. This resulted in earlier thermal breakthroughs because conductive heat transfer between the reservoir and the surrounding rock matrix was not accounted for. After correcting the model and rerunning the simulations, we found that the correction affects only the reported numerical values. The overall trends, relative comparisons between scenarios, interpretations, and scientific conclusions presented in the paper remain unchanged. The inclusion of rock thermal conductivity primarily delays thermal breakthrough and therefore we extend the simulation time from 30 years to 80 years in base case to capture long-term thermal behavior. The sensitivity analysis indicates that the ranking of parameter importance remains consistent, whereas the magnitude of the importance scores increase after introducing the rock thermal conductivity and rock heat capacity.
...
The authors regret that during a post-publication review of the numerical model, we identified that rock thermal conductivity and rock heat capacity were not transferred properly for the ensemble run and were set to zero for the energy conservation equation. This resulted in earlier thermal breakthroughs because conductive heat transfer between the reservoir and the surrounding rock matrix was not accounted for. After correcting the model and rerunning the simulations, we found that the correction affects only the reported numerical values. The overall trends, relative comparisons between scenarios, interpretations, and scientific conclusions presented in the paper remain unchanged. The inclusion of rock thermal conductivity primarily delays thermal breakthrough and therefore we extend the simulation time from 30 years to 80 years in base case to capture long-term thermal behavior. The sensitivity analysis indicates that the ranking of parameter importance remains consistent, whereas the magnitude of the importance scores increase after introducing the rock thermal conductivity and rock heat capacity.
Journal article
(2026)
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David Geerts, Wen Liu, Alexandros Daniilidis, Philip J. Vardon, Gert Jan Kramer
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.
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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.
Groundwater-induced advective heat transfer in U-shaped closed-loop geothermal system
Application for a Red Sea rift sedimentary basin
Journal article
(2025)
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Omar Alobaid, C. Justin Ezekiel, Alexandros Daniilidis, Thomas Finkbeiner, P. Martin Mai
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.
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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.
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.
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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.
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.
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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.
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.
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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.
Journal article
(2025)
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David Geerts, Alexandros Daniilidis, Gert Jan Kramer, Martin Bloemendal, Wen Liu
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.
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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.
Journal article
(2025)
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Entela Kane, Olwijn Leeuwenburgh, Gerard Joosten, Alexandros Daniilidis, David Bruhn
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.
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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.
Journal article
(2025)
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David Geerts, Alexandros Daniilidis, Gert Jan Kramer, Martin Bloemendal, Wen Liu
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.
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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.
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.
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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.
Workflows, Data and Modelling Technologies for Geothermal Heat Exploration
From Industry Standard to State-of-the-Art
Conference paper
(2025)
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L. Janku, G. Hampson, P. Bruna, H. Guðmundsdóttir, T. Fischer, G. de Vries, S. Bakrac, P. Haffinger, V. Nogales, L. Tryggvadóttir, A. Peterhaensel, H. Claridge, S. Geiger, F. Dekker, M. Bentley, T. Wynn, A. Babasafari, Matthew Jackson, A. Daniilidis, B. Lamy-Chappuis, P. Jimenez Hernandez, T. Driesner, C. Glaas, J. Vlček
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.
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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.
From outcrop observations to dynamic simulations
An efficient workflow for generating ensembles of geologically plausible fracture networks and assessing their impact on flow and transport
Journal article
(2025)
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Elahe Kamel Targhi, Pierre-Olivier Bruna, Alexandros Daniilidis, Guillaume Rongier, Sebastian Geiger
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.
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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.