S. Geiger
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248 records found
1
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Journal article
(2026)
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Hariharan Ramachandran, Iain de Jonge-Anderson, Ikhwanul Hafizi Musa, Uisdean Nicholson, Chee Phuat Tan, S. Geiger, Florian Doster
Simulating the fluid flow along fault zones at different scales is essential for predicting the CO2 leakage and containment during injection and storage. However, this can be challenging, especially in the early stages of a storage project when knowledge of the reservoir and caprock is limited and the cost of obtaining the relevant data is high. This study develops a tool for fast screening of fault leakage at the site screening stage. The tool uses a vertically integrated reservoir model coupled with a newly developed upscaled fault leakage function based on source/sink relations. The fault is conceptualized as an increased vertical permeability through the caprock due to the presence of a fracture network in the damage zone and a reduced horizontal permeability in the reservoir due to fault throw and presence of a low-permeability fault core. The proposed tool is validated against numerical simulations demonstrating strong agreement in predicting leakage rates under varying reservoir conditions. The model's capabilities are further tested through simulation cases, including a field-scale application in the Malay Basin. These cases revealed key insights into the roles of fault permeability and fault capillary entry pressure in controlling leakage and highlighted the importance of accurately characterizing these properties to mitigate risks. The computationally efficient model presented in this study is a valuable tool for quantifying uncertainties in key fault parameters, and other constitutive relations that affect the behavior of the storage reservoir and potential fault leakage.
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Simulating the fluid flow along fault zones at different scales is essential for predicting the CO2 leakage and containment during injection and storage. However, this can be challenging, especially in the early stages of a storage project when knowledge of the reservoir and caprock is limited and the cost of obtaining the relevant data is high. This study develops a tool for fast screening of fault leakage at the site screening stage. The tool uses a vertically integrated reservoir model coupled with a newly developed upscaled fault leakage function based on source/sink relations. The fault is conceptualized as an increased vertical permeability through the caprock due to the presence of a fracture network in the damage zone and a reduced horizontal permeability in the reservoir due to fault throw and presence of a low-permeability fault core. The proposed tool is validated against numerical simulations demonstrating strong agreement in predicting leakage rates under varying reservoir conditions. The model's capabilities are further tested through simulation cases, including a field-scale application in the Malay Basin. These cases revealed key insights into the roles of fault permeability and fault capillary entry pressure in controlling leakage and highlighted the importance of accurately characterizing these properties to mitigate risks. The computationally efficient model presented in this study is a valuable tool for quantifying uncertainties in key fault parameters, and other constitutive relations that affect the behavior of the storage reservoir and potential fault leakage.
Core-scale evaluation of CO2 reinjection feasibility in a carbonate reservoir–caprock system
The S Field case study, Sarawak Basin, Malaysia
Journal article
(2026)
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Sahriza Salwani Md Shah, David N. Dewhurst, Ausama Giwelli, Mark D. Raven, Siti Syareena M. Ali, Sebastian Geiger, Andreas Busch
This study evaluates the feasibility of reinjecting separated CO₂ into its source carbonate reservoir, the high-temperature, high-pressure S Field in the Sarawak Basin, offshore Malaysia. The reservoir gas comprises ∼30% CH₄ and ∼70% CO₂. We combine mineralogical, petrophysical and geomechanical analyses to assess reservoir integrity and caprock sealing performance under in situ conditions. The calcite-dominated gas zone exhibits high porosity (>30%), whereas the underlying aquifer zone is co-dominated by calcite and dolomite with lower porosity (20–25%) and higher strength. The primary caprock (Seal A), a 500 m-thick Miocene mudrock–siltstone unit, has porosities of 2–10% and low permeability, with illite and quartz as dominant minerals. Laboratory experiments exposed reservoir and caprock samples to CO₂-charged brines at reservoir pressure (30 MPa) and temperature (150°C) for up to six months. Post-reaction analyses revealed only minor changes in mineralogy, porosity, permeability and mechanical strength. These results indicate limited CO₂–water–rock reactivity and confirm the mechanical and geochemical stability of both reservoir and caprock. The findings support the viability of CO₂ reinjection and long-term geological storage in the S Field, providing a benchmark case for carbonate-hosted carbon capture and storage systems in SE Asia.
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This study evaluates the feasibility of reinjecting separated CO₂ into its source carbonate reservoir, the high-temperature, high-pressure S Field in the Sarawak Basin, offshore Malaysia. The reservoir gas comprises ∼30% CH₄ and ∼70% CO₂. We combine mineralogical, petrophysical and geomechanical analyses to assess reservoir integrity and caprock sealing performance under in situ conditions. The calcite-dominated gas zone exhibits high porosity (>30%), whereas the underlying aquifer zone is co-dominated by calcite and dolomite with lower porosity (20–25%) and higher strength. The primary caprock (Seal A), a 500 m-thick Miocene mudrock–siltstone unit, has porosities of 2–10% and low permeability, with illite and quartz as dominant minerals. Laboratory experiments exposed reservoir and caprock samples to CO₂-charged brines at reservoir pressure (30 MPa) and temperature (150°C) for up to six months. Post-reaction analyses revealed only minor changes in mineralogy, porosity, permeability and mechanical strength. These results indicate limited CO₂–water–rock reactivity and confirm the mechanical and geochemical stability of both reservoir and caprock. The findings support the viability of CO₂ reinjection and long-term geological storage in the S Field, providing a benchmark case for carbonate-hosted carbon capture and storage systems in SE Asia.
Journal article
(2026)
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Guofeng Song, Denis Voskov, Hemmo A. Abels, Philip J. Vardon, Sebastian Geiger
Geothermal energy is a key option for decarbonizing heating and cooling in the energy transition. Forecasting geothermal production has inherent uncertainty due to the heterogeneity of geological formations that host the geothermal resource and the limited data available to characterize and quantify these heterogeneities. This uncertainty leads to operational risks such as early thermal breakthrough. Identifying the most valuable monitoring data and data acquisition strategies for operators is key to constraining uncertainties and ultimately de-risking operations in a reliable and cost-effective way. Data-worth analysis quantifies the value of data provided by existing observations or proposed data collection strategies. This study combines Ensemble Smoother with Multiple Data Assimilation and data-worth analysis to constrain uncertainty in production forecasts and reservoir response for a geothermal doublet system located in a clastic, channelized fluvial reservoir. The main monitoring data includes production temperature, injection pressure, and temperature and pressure profiles along the well paths. We show that production temperature and injection pressure alone only can constrain uncertainties in production forecasts. Using observations of well temperature and pressure profiles demonstrates a threefold increase in data worth, which improves both the quantification of production forecasts and reservoir dynamics. At constant injection rates in doublet system, temperature profiles monitored along the wells provide higher data worth compared to pressure profiles. Data from a deviated monitoring borehole is advantageous. Early-time (first year) observations of temperature and pressure profiles along the injector, producer, and monitoring borehole already constrain production uncertainty prior to thermal breakthrough. Data-worth analysis is shown to be most beneficial when conducted across multiple plausible geological scenarios to ensure a more reliable assessment of collection strategies. The findings of this study yield insight into designing informative data-acquisition strategies for direct-use geothermal systems.
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Geothermal energy is a key option for decarbonizing heating and cooling in the energy transition. Forecasting geothermal production has inherent uncertainty due to the heterogeneity of geological formations that host the geothermal resource and the limited data available to characterize and quantify these heterogeneities. This uncertainty leads to operational risks such as early thermal breakthrough. Identifying the most valuable monitoring data and data acquisition strategies for operators is key to constraining uncertainties and ultimately de-risking operations in a reliable and cost-effective way. Data-worth analysis quantifies the value of data provided by existing observations or proposed data collection strategies. This study combines Ensemble Smoother with Multiple Data Assimilation and data-worth analysis to constrain uncertainty in production forecasts and reservoir response for a geothermal doublet system located in a clastic, channelized fluvial reservoir. The main monitoring data includes production temperature, injection pressure, and temperature and pressure profiles along the well paths. We show that production temperature and injection pressure alone only can constrain uncertainties in production forecasts. Using observations of well temperature and pressure profiles demonstrates a threefold increase in data worth, which improves both the quantification of production forecasts and reservoir dynamics. At constant injection rates in doublet system, temperature profiles monitored along the wells provide higher data worth compared to pressure profiles. Data from a deviated monitoring borehole is advantageous. Early-time (first year) observations of temperature and pressure profiles along the injector, producer, and monitoring borehole already constrain production uncertainty prior to thermal breakthrough. Data-worth analysis is shown to be most beneficial when conducted across multiple plausible geological scenarios to ensure a more reliable assessment of collection strategies. The findings of this study yield insight into designing informative data-acquisition strategies for direct-use geothermal systems.
Conference paper
(2026)
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Q. Zhang, S. Geiger, J.E. Storms, M.D. Jackson, C. Jacquemyn, G.J. Hampson, A.W. Martinius
Stratigraphic and diagenetic heterogeneities exert a strong control on CO₂ plume dynamics and long-term storage performance in shallow-marine reservoirs. Using multiphase flow simulations conditioned to a geologically realistic reservoir model, this study demonstrates that repeated cemented barriers and stratigraphic baffles significantly slow vertical plume ascent and promote lateral plume spreading. This behavior increases overall CO₂ storage through a combined action of stratigraphic trapping, capillary pinning, residual trapping, and dissolution. Plume dispersion and local spreading were found to strongly enhance dissolution trapping. By subdividing a single buoyant plume into smaller, vertically confined accumulations, stratigraphic barriers increase CO₂–brine interfacial area and prolong residence times, resulting in substantially greater solubility trapping than would occur in homogeneous reservoir models. Structural configuration further modulates this behavior, with reduced buoyant drive leading to higher dissolved fractions. The strong sensitivity of plume migration and trapping behavior to geological heterogeneity highlights the importance of fitness-for-purpose modeling in CCS site assessment. While simplified models may be appropriate for early-stage screening, they may fail to capture critical plume dispersion and trapping processes if applied beyond their intended scope. These findings emphasize the need for detailed geological characterization and advanced modeling approaches tailored specifically to CCS applications.
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Stratigraphic and diagenetic heterogeneities exert a strong control on CO₂ plume dynamics and long-term storage performance in shallow-marine reservoirs. Using multiphase flow simulations conditioned to a geologically realistic reservoir model, this study demonstrates that repeated cemented barriers and stratigraphic baffles significantly slow vertical plume ascent and promote lateral plume spreading. This behavior increases overall CO₂ storage through a combined action of stratigraphic trapping, capillary pinning, residual trapping, and dissolution. Plume dispersion and local spreading were found to strongly enhance dissolution trapping. By subdividing a single buoyant plume into smaller, vertically confined accumulations, stratigraphic barriers increase CO₂–brine interfacial area and prolong residence times, resulting in substantially greater solubility trapping than would occur in homogeneous reservoir models. Structural configuration further modulates this behavior, with reduced buoyant drive leading to higher dissolved fractions. The strong sensitivity of plume migration and trapping behavior to geological heterogeneity highlights the importance of fitness-for-purpose modeling in CCS site assessment. While simplified models may be appropriate for early-stage screening, they may fail to capture critical plume dispersion and trapping processes if applied beyond their intended scope. These findings emphasize the need for detailed geological characterization and advanced modeling approaches tailored specifically to CCS applications.
Conference paper
(2026)
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M. Knott, R. Cox, T. Hornes, L. Kuusik, E. Meen Hidalgo-Chacón, J.D. Jansen, M. Matsumoto, S. Ragnarsson, S. Geiger
Operational instability in high-enthalpy geothermal wells remains a key challenge for sustainable energy production, particularly in fractured volcanic reservoirs such as those frequently found in Iceland. This work investigates pressure and flow rate oscillations observed in production wells at the Hengill geothermal area, with a central focus on the Multiple Feed Zone theory as the dominant explanatory framework. This extended abstract synthesises the main findings of the conducted study on well oscillating phenomena, analysed through conceptual models, well data interpretation, and numerical simulations. The results demonstrate that interactions between feed zones of differing pressure, temperature, and permeability can induce self-sustained oscillations without requiring classical internal flow instability mechanisms. These findings have significant implications for well design, monitoring, and control strategies in high-enthalpy geothermal systems.
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Operational instability in high-enthalpy geothermal wells remains a key challenge for sustainable energy production, particularly in fractured volcanic reservoirs such as those frequently found in Iceland. This work investigates pressure and flow rate oscillations observed in production wells at the Hengill geothermal area, with a central focus on the Multiple Feed Zone theory as the dominant explanatory framework. This extended abstract synthesises the main findings of the conducted study on well oscillating phenomena, analysed through conceptual models, well data interpretation, and numerical simulations. The results demonstrate that interactions between feed zones of differing pressure, temperature, and permeability can induce self-sustained oscillations without requiring classical internal flow instability mechanisms. These findings have significant implications for well design, monitoring, and control strategies in high-enthalpy geothermal systems.
Journal article
(2026)
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Guofeng Song, Sebastian Geiger, Denis Voskov, Hemmo A. Abels, Philip J. Vardon
Geothermal energy has the potential to decarbonize heating, cooling, and power production. However, managing the efficient and sustainable exploitation of geothermal resources is challenging due to the limited data availability, which restricts our ability to characterize and quantify the multi-scale, hierarchical geological structures of the hosting reservoirs. In this study, we propose a scenario-based data assimilation framework that enables the efficient modelling of multiple complex geological scenarios and is linked to flow and heat transfer simulations for subsequent uncertainty analysis. This framework is based on an ensemble smoother with multiple data assimilation (ESMDA) and demonstrated on a channelized fluvial geothermal reservoir. By improving the open-source Rapid Reservoir Modelling (RRM) tool, we efficiently create multiple deterministic fluvial geothermal reservoir scenarios that honors facies along well paths in a probabilistic manner by randomly selecting, cropping, and stacking channelized layers from the layer template library. Petrophysical properties for each scenario are then modelled using geostatistics to generate a geologically plausible and sufficiently diverse ensemble of reservoir realizations. The multiple scenarios and corresponding ensemble realizations are then subjected to heat and fluid flow simulations using the open-source Delft Advanced Research Terra Simulator (open-DARTS) to quantify the uncertainty of production temperatures and reservoir pressures. Finally, ESMDA is employed to assimilate temperature and pressure profiles at the injection well, monitoring borehole, and production well across all members of the ensemble realizations for the different geological scenarios. We demonstrate the applicability of our framework using a synthetic, yet geologically consistent, case study of a low-enthalpy geothermal system where heat is produced from a geothermal doublet located in a channelized fluvial sandstone reservoir. The framework enables the falsification of geological scenarios with poor data assimilation performance that is unlikely to reflect the actual reservoir architecture, and supports the identification of plausible geological scenarios that are more likely to represent the subsurface geology based on the deviation of modelled and observed well temperature and pressure profiles. The workflow offers an efficient way to constrain geological uncertainties inherent to geologically complex geothermal reservoirs and improve the forecasting of production temperatures and pressure differences.
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Geothermal energy has the potential to decarbonize heating, cooling, and power production. However, managing the efficient and sustainable exploitation of geothermal resources is challenging due to the limited data availability, which restricts our ability to characterize and quantify the multi-scale, hierarchical geological structures of the hosting reservoirs. In this study, we propose a scenario-based data assimilation framework that enables the efficient modelling of multiple complex geological scenarios and is linked to flow and heat transfer simulations for subsequent uncertainty analysis. This framework is based on an ensemble smoother with multiple data assimilation (ESMDA) and demonstrated on a channelized fluvial geothermal reservoir. By improving the open-source Rapid Reservoir Modelling (RRM) tool, we efficiently create multiple deterministic fluvial geothermal reservoir scenarios that honors facies along well paths in a probabilistic manner by randomly selecting, cropping, and stacking channelized layers from the layer template library. Petrophysical properties for each scenario are then modelled using geostatistics to generate a geologically plausible and sufficiently diverse ensemble of reservoir realizations. The multiple scenarios and corresponding ensemble realizations are then subjected to heat and fluid flow simulations using the open-source Delft Advanced Research Terra Simulator (open-DARTS) to quantify the uncertainty of production temperatures and reservoir pressures. Finally, ESMDA is employed to assimilate temperature and pressure profiles at the injection well, monitoring borehole, and production well across all members of the ensemble realizations for the different geological scenarios. We demonstrate the applicability of our framework using a synthetic, yet geologically consistent, case study of a low-enthalpy geothermal system where heat is produced from a geothermal doublet located in a channelized fluvial sandstone reservoir. The framework enables the falsification of geological scenarios with poor data assimilation performance that is unlikely to reflect the actual reservoir architecture, and supports the identification of plausible geological scenarios that are more likely to represent the subsurface geology based on the deviation of modelled and observed well temperature and pressure profiles. The workflow offers an efficient way to constrain geological uncertainties inherent to geologically complex geothermal reservoirs and improve the forecasting of production temperatures and pressure differences.
Journal article
(2026)
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Zhenkai Bo, Ahmed H. Elsheikh, Hannah P. Menke, Julien Maes, Sebastian Geiger, Muhammad Z. Kashim, Zainol A.A. Bakar, Kamaljit Singh
Carbonate reservoirs offer significant capacity for subsurface carbon storage, oil production, underground hydrogen storage, geothermal energy, and groundwater flow. Accurate characterization of fluid flow behavior in these rocks is therefore critical for both resource recovery and emissions mitigation, yet it remains challenging due to their inherent heterogeneity. The wide range of carbonate pore-throat size distribution, spanning from nm to cm, hinders a comprehensive pore structure characterization with conventional single-scale X-ray computed tomography (micro-CT) images. Multi-scale imaging, which refers to acquiring CT images at both macro and micro resolutions, has emerged as a practical strategy to bridge this gap. In practice, nm-scale CT imaging requires the physical extraction of mini-plugs from the macro core sample, while the selection of drilling locations remains largely subjective, lacking a quantitative framework for rigorous decision-making. Digital rock modeling can assist this decision-making process by predicting flow properties from macro-scale sample images. However, its computational cost remains prohibitive for routine use. To facilitate an efficient, digitalized sub-sampling decision-making workflow, we propose a machine learning-enhanced data assimilation framework that leverages experimental drainage relative permeability measurements to achieve efficient characterization of micro-scale structures. We train a dense neural network (DNN) as a proxy to a multi-scale pore network simulator and couple it with an ensemble smoother with multiple data assimilation (ESMDA) algorithm. The DNN-ESMDA framework simultaneously infers the CO2-brine drainage relative permeability of microporosity phases with associated uncertainty estimation, revealing the relative importance of each rock phase and guiding future characterization. Our DNN-ESMDA framework achieves a significant computational speedup, reducing inference time from thousands of hours to seconds compared to conventional multi-scale numerical simulation. The machine learning-enhanced ESMDA framework therefore provides a practical approach for improving the multi-scale imaging workflow of carbonates.
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Carbonate reservoirs offer significant capacity for subsurface carbon storage, oil production, underground hydrogen storage, geothermal energy, and groundwater flow. Accurate characterization of fluid flow behavior in these rocks is therefore critical for both resource recovery and emissions mitigation, yet it remains challenging due to their inherent heterogeneity. The wide range of carbonate pore-throat size distribution, spanning from nm to cm, hinders a comprehensive pore structure characterization with conventional single-scale X-ray computed tomography (micro-CT) images. Multi-scale imaging, which refers to acquiring CT images at both macro and micro resolutions, has emerged as a practical strategy to bridge this gap. In practice, nm-scale CT imaging requires the physical extraction of mini-plugs from the macro core sample, while the selection of drilling locations remains largely subjective, lacking a quantitative framework for rigorous decision-making. Digital rock modeling can assist this decision-making process by predicting flow properties from macro-scale sample images. However, its computational cost remains prohibitive for routine use. To facilitate an efficient, digitalized sub-sampling decision-making workflow, we propose a machine learning-enhanced data assimilation framework that leverages experimental drainage relative permeability measurements to achieve efficient characterization of micro-scale structures. We train a dense neural network (DNN) as a proxy to a multi-scale pore network simulator and couple it with an ensemble smoother with multiple data assimilation (ESMDA) algorithm. The DNN-ESMDA framework simultaneously infers the CO2-brine drainage relative permeability of microporosity phases with associated uncertainty estimation, revealing the relative importance of each rock phase and guiding future characterization. Our DNN-ESMDA framework achieves a significant computational speedup, reducing inference time from thousands of hours to seconds compared to conventional multi-scale numerical simulation. The machine learning-enhanced ESMDA framework therefore provides a practical approach for improving the multi-scale imaging workflow of carbonates.
Evaluation of operational strategies for underground hydrogen storage in depleted gas fields under diverse geological scenarios
Guidelines for site screening and development planning
Journal article
(2026)
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Ana Loyola, Denis Voskov, Rouhi Farajzadeh, Karin de Borst, Sebastian Geiger
Underground hydrogen storage in depleted gas fields is a potential solution for large-scale, seasonal storage of hydrogen, in support of the decarbonization of energy systems and other industrial activities. Its viability depends on the performance of the storage operations, which is influenced by the interaction between reservoir geology and operational strategies. However, general guidelines for development planning that account for geological uncertainty are still lacking. In addition, existing site screening criteria remain limited in that they do not account for how operational decisions can alter the suitability of a reservoir geology for hydrogen storage. Here, we employ a numerical model of flow and transport to evaluate a set of operational strategies in varying geological scenarios for depleted methane gas reservoirs of the Bunter Sandstone, an important formation in the North Sea. We investigate the following strategies for their impact on performance and interaction with geological features that are common in the Bunter sandstone: depletion level, injected hydrogen mass, cushion gas, well perforation, idle period, production rates, and methane reinjection. We found that depletion level, injected mass, and well perforation interact strongly with geology and are critical for site selection. The methane reinjection strategy provides pressure support that increases hydrogen production, though at the cost of purity in the long-term. Furthermore, cushion gas strategies show significant optimization potential but limited interaction with geology, whereas the duration of the idle period and target rates have low optimization potential. Based on these findings, we propose a site selection and development planning framework for underground hydrogen storage in depleted gas fields. The site selection phase introduces a novel screening criterion, the gravity–purity number, which integrates geological and operational considerations. The development phase provides criteria and guidelines for planning operational strategies, and establishes a hierarchy based on their optimization potential.
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Underground hydrogen storage in depleted gas fields is a potential solution for large-scale, seasonal storage of hydrogen, in support of the decarbonization of energy systems and other industrial activities. Its viability depends on the performance of the storage operations, which is influenced by the interaction between reservoir geology and operational strategies. However, general guidelines for development planning that account for geological uncertainty are still lacking. In addition, existing site screening criteria remain limited in that they do not account for how operational decisions can alter the suitability of a reservoir geology for hydrogen storage. Here, we employ a numerical model of flow and transport to evaluate a set of operational strategies in varying geological scenarios for depleted methane gas reservoirs of the Bunter Sandstone, an important formation in the North Sea. We investigate the following strategies for their impact on performance and interaction with geological features that are common in the Bunter sandstone: depletion level, injected hydrogen mass, cushion gas, well perforation, idle period, production rates, and methane reinjection. We found that depletion level, injected mass, and well perforation interact strongly with geology and are critical for site selection. The methane reinjection strategy provides pressure support that increases hydrogen production, though at the cost of purity in the long-term. Furthermore, cushion gas strategies show significant optimization potential but limited interaction with geology, whereas the duration of the idle period and target rates have low optimization potential. Based on these findings, we propose a site selection and development planning framework for underground hydrogen storage in depleted gas fields. The site selection phase introduces a novel screening criterion, the gravity–purity number, which integrates geological and operational considerations. The development phase provides criteria and guidelines for planning operational strategies, and establishes a hierarchy based on their optimization potential.
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.
Pore-scale analysis of hydrogen-water displacement in sandstones
A comparison of pore-network modelling and flow visualisation experiments
Journal article
(2026)
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Zaid Jangda, Tom Bultreys, Zeyun Jiang, Sajjad Foroughi, Hannah Menke, Andreas Busch, Sebastian Geiger, Kamaljit Singh
Hydrogen–water displacement in porous rocks involves capillary-dominated multiphase-flow processes at the pore scale that are critical for understanding fluid distribution, trapping, and recovery behaviour. Three-dimensional pore-scale flow visualisation experiments provide direct insight into these processes but are resource intensive and technically challenging. Pore-network models offer a computationally efficient alternative for simulating capillary-dominated multiphase flow, but their accuracy depends on how well-simplified displacement rules represent real pore-scale behaviour. This work presents a direct pore-by-pore comparison between experimentally observed displacement events and predictions from a quasi-static pore-network model. The comparison enables evaluation of the model’s simplifying assumptions and its ability to reproduce pore-scale displacement behaviour across contrasting rock types, including a homogeneous Bentheimer sandstone and a layered Clashach sandstone. The model was calibrated to match experimental end-state saturations, and its performance was evaluated using spatial saturation distributions and pore-occupancy statistics. The pore-network model shows good agreement with experimental observations for the homogeneous rock, particularly during drainage. It is subsequently used to analyse additional scenarios, including cyclic hydrogen injection and withdrawal and wettability variations, providing insight into capillary pressure behaviour and residual saturation trends. In contrast, for the heterogeneous rock, the model does not fully capture the trapping and fluid redistribution observed experimentally, indicating limitations in representing fine-scale heterogeneity. Overall, the results identify the conditions under which the quasi-static pore-network model can reliably represent hydrogen–water displacement and where its simplifying assumptions become limiting, providing guidance for its application in pore-scale multiphase-flow research.
...
Hydrogen–water displacement in porous rocks involves capillary-dominated multiphase-flow processes at the pore scale that are critical for understanding fluid distribution, trapping, and recovery behaviour. Three-dimensional pore-scale flow visualisation experiments provide direct insight into these processes but are resource intensive and technically challenging. Pore-network models offer a computationally efficient alternative for simulating capillary-dominated multiphase flow, but their accuracy depends on how well-simplified displacement rules represent real pore-scale behaviour. This work presents a direct pore-by-pore comparison between experimentally observed displacement events and predictions from a quasi-static pore-network model. The comparison enables evaluation of the model’s simplifying assumptions and its ability to reproduce pore-scale displacement behaviour across contrasting rock types, including a homogeneous Bentheimer sandstone and a layered Clashach sandstone. The model was calibrated to match experimental end-state saturations, and its performance was evaluated using spatial saturation distributions and pore-occupancy statistics. The pore-network model shows good agreement with experimental observations for the homogeneous rock, particularly during drainage. It is subsequently used to analyse additional scenarios, including cyclic hydrogen injection and withdrawal and wettability variations, providing insight into capillary pressure behaviour and residual saturation trends. In contrast, for the heterogeneous rock, the model does not fully capture the trapping and fluid redistribution observed experimentally, indicating limitations in representing fine-scale heterogeneity. Overall, the results identify the conditions under which the quasi-static pore-network model can reliably represent hydrogen–water displacement and where its simplifying assumptions become limiting, providing guidance for its application in pore-scale multiphase-flow research.
Journal article
(2026)
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Mathias José Kreutz Erdtmann, Filipe Silva Lira, Sebastian Geiger, Hadi Hajibeygi
Multiscale simulation frameworks are essential to quantify the CO2 trapping and migration in large-scale saline aquifers, which entail highly-resolved fine-scale heterogeneous properties. However, classical upscaling approaches which aim to define effective properties on larger grid sizes can lead to significant and systematic overestimation of the solubility and residual trapping mechanisms. Reliable assessment of these two trapping mechanisms is crucial to ensure the integrity of the storage process and properly mitigate the leakage risks. Therefore, it is essential to develop advanced simulation technologies that are both accurate and efficient (i.e., scalable) for simulation of complex CO2 plume dynamics within large-scale heterogeneous reservoir models. To overcome this challenge, in this work three advanced strategies are developed and investigated: Effective Values (EV) for parameters, Local Grid Refinement (LGR) and Algebraic Dynamic Multilevel (ADM). The numerical investigations specially include a set of consistent models in the Ponta Aguda saline aquifer, with a total area of 40,000 km2[jls-end-space/], located offshore the Brazilian coast. The results indicate that the ADM is a promising method, delivering stable and robust results in a representative section of the field. This encourages further extensions of this method for real-field deployment. Specially, LGR and EV are found to be limited in their scopes for field simulations, since they depend on a matching pre-procedure (against a reference solution) for their upscaled parameters before any new simulations can be run. In addition, their tuned parameters cannot be transferred from one model to another. ADM, on the other hand, does not require any upscaling procedure, as the multiscale basis functions allow for consistent mapping across resolutions.
...
Multiscale simulation frameworks are essential to quantify the CO2 trapping and migration in large-scale saline aquifers, which entail highly-resolved fine-scale heterogeneous properties. However, classical upscaling approaches which aim to define effective properties on larger grid sizes can lead to significant and systematic overestimation of the solubility and residual trapping mechanisms. Reliable assessment of these two trapping mechanisms is crucial to ensure the integrity of the storage process and properly mitigate the leakage risks. Therefore, it is essential to develop advanced simulation technologies that are both accurate and efficient (i.e., scalable) for simulation of complex CO2 plume dynamics within large-scale heterogeneous reservoir models. To overcome this challenge, in this work three advanced strategies are developed and investigated: Effective Values (EV) for parameters, Local Grid Refinement (LGR) and Algebraic Dynamic Multilevel (ADM). The numerical investigations specially include a set of consistent models in the Ponta Aguda saline aquifer, with a total area of 40,000 km2[jls-end-space/], located offshore the Brazilian coast. The results indicate that the ADM is a promising method, delivering stable and robust results in a representative section of the field. This encourages further extensions of this method for real-field deployment. Specially, LGR and EV are found to be limited in their scopes for field simulations, since they depend on a matching pre-procedure (against a reference solution) for their upscaled parameters before any new simulations can be run. In addition, their tuned parameters cannot be transferred from one model to another. ADM, on the other hand, does not require any upscaling procedure, as the multiscale basis functions allow for consistent mapping across resolutions.
Preprint
(2026)
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Zaid Jangda, Tom Bultreys, Zeyun Jiang, Sajjad Foroughi, Hannah P. Menke, Andreas Busch, Sebastian Geiger, Kamaljit Singh
Underground hydrogen storage in porous formations is a promising solution for large-scale energy storage. Understanding hydrogen flow and trapping at the pore-scale is crucial for assessing storage capacity and recovery efficiency. While pore-scale flow visualisation experiments provide realistic insights, they are resource intensive and technically challenging. Pore-network models offer a computationally efficient tool for simulating multiphase flow in porous media and can serve as a valuable complement to pore-scale experiments. However, their accuracy remains a key uncertainty and must be evaluated for future application. This study evaluates the performance of a quasi-static pore-network model by comparing its predictions against three-dimensional pore-scale hydrogen flow visualisation experiments in a homogeneous Bentheimer sandstone and a layered Clashach sandstone. The model was calibrated to match experimental end-state saturations, and its performance was evaluated through comparisons of spatial saturation profiles and pore occupancy. The novelty of this study lies in the direct comparison of hydrogen displacement between pore-scale experimental observations and pore-network model simulations, providing an assessment of model performance under varying degrees of rock heterogeneity relevant to underground hydrogen storage. The pore-network model shows good agreement with experimental observations for the homogeneous rock, particularly during drainage, and is subsequently used to analyse additional scenarios, including cyclic hydrogen injection and withdrawal, and wettability variations. These simulations provide insights into capillary pressure behaviour and residual saturation trends. In contrast, for the heterogeneous and layered Clashach sandstone, the model fails to capture the trapping and fluid redistribution observed experimentally during imbibition, revealing limitations in modelling fine-scale heterogeneity.
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Underground hydrogen storage in porous formations is a promising solution for large-scale energy storage. Understanding hydrogen flow and trapping at the pore-scale is crucial for assessing storage capacity and recovery efficiency. While pore-scale flow visualisation experiments provide realistic insights, they are resource intensive and technically challenging. Pore-network models offer a computationally efficient tool for simulating multiphase flow in porous media and can serve as a valuable complement to pore-scale experiments. However, their accuracy remains a key uncertainty and must be evaluated for future application. This study evaluates the performance of a quasi-static pore-network model by comparing its predictions against three-dimensional pore-scale hydrogen flow visualisation experiments in a homogeneous Bentheimer sandstone and a layered Clashach sandstone. The model was calibrated to match experimental end-state saturations, and its performance was evaluated through comparisons of spatial saturation profiles and pore occupancy. The novelty of this study lies in the direct comparison of hydrogen displacement between pore-scale experimental observations and pore-network model simulations, providing an assessment of model performance under varying degrees of rock heterogeneity relevant to underground hydrogen storage. The pore-network model shows good agreement with experimental observations for the homogeneous rock, particularly during drainage, and is subsequently used to analyse additional scenarios, including cyclic hydrogen injection and withdrawal, and wettability variations. These simulations provide insights into capillary pressure behaviour and residual saturation trends. In contrast, for the heterogeneous and layered Clashach sandstone, the model fails to capture the trapping and fluid redistribution observed experimentally during imbibition, revealing limitations in modelling fine-scale heterogeneity.
Conference paper
(2026)
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C. Jacquemyn, M.D. Jackson, G.J. Hampson, D. Petrosvkyy, Q. Zhang, J. Storms, S. Geiger, A.W. Martinius
Assessment of CO2 storage capacity in shallow marine reservoirs must be underpinned by understanding of the impact of geological heterogeneity (Zhang et al., 2025). Heterogeneity is present at multiple scales from laminae (cm’s) to facies (m’s), to facies associations (10’s m), to parasequences (100’s m), to parasequence set (km’s) scale. Using a multiscale representative elementary volume (REV) approach, we investigate how heterogeneity at the facies and facies association scales impact properties such as effective permeability and effective relative permeability that are key inputs for simulating storage at reservoir scale. Facies-scale (m’s) heterogeneity clearly impacts effective permeability values and effective relative permeability curves, and their anisotropy. Models that capture the heterogeneity architecture of facies in 3D form a robust basis to link plug-scale (cm’s) measurements to larger scales, providing values that are potentially directly usable in flow simulation assessing CO2 flow dynamics and trapping for CCS. Upscaled relative permeability curves are anisotropic and can vary outside the bounds of the input curves as a direct result of 3D heterogeneity. This finding demonstrates that sedimentological heterogeneity affects CO2 flow and storage and illustrates how core-plug-scale measurements can be used to make predictions that incorporate the effects of facies-scale heterogeneity at larger scales.
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Assessment of CO2 storage capacity in shallow marine reservoirs must be underpinned by understanding of the impact of geological heterogeneity (Zhang et al., 2025). Heterogeneity is present at multiple scales from laminae (cm’s) to facies (m’s), to facies associations (10’s m), to parasequences (100’s m), to parasequence set (km’s) scale. Using a multiscale representative elementary volume (REV) approach, we investigate how heterogeneity at the facies and facies association scales impact properties such as effective permeability and effective relative permeability that are key inputs for simulating storage at reservoir scale. Facies-scale (m’s) heterogeneity clearly impacts effective permeability values and effective relative permeability curves, and their anisotropy. Models that capture the heterogeneity architecture of facies in 3D form a robust basis to link plug-scale (cm’s) measurements to larger scales, providing values that are potentially directly usable in flow simulation assessing CO2 flow dynamics and trapping for CCS. Upscaled relative permeability curves are anisotropic and can vary outside the bounds of the input curves as a direct result of 3D heterogeneity. This finding demonstrates that sedimentological heterogeneity affects CO2 flow and storage and illustrates how core-plug-scale measurements can be used to make predictions that incorporate the effects of facies-scale heterogeneity at larger scales.
Long-term geothermal production is subject to considerable uncertainty due to limited data availability and inherent geological heterogeneity. While observation and data acquisition improve our understanding of the reservoir, they also contribute significantly to project costs. It is essential to identify the most informative observation strategy. In this study, we apply a previously developed scenario-based data assimilation framework that integrates rapid geological modelling, efficient numerical simulation, and Ensemble Smoother with Multiple Data Assimilation (ESMDA) to constrain uncertainties in reservoir properties and production forecasts to a synthetic but geologically realistic fluvial geothermal system and conduct a data worth analysis to evaluate the impact of different observations (production temperature and injection pressure, well temperature and pressure profiles, etc.) on uncertainty reduction. Results show that production temperature and injection pressure alone, though cost-effective, are insufficient to significantly reduce uncertainties in reservoir performance forecasts. In contrast, well temperature and pressure profiles exhibit substantially higher data worth, leading to much better-constrained predictions. Moreover, incorporating a monitoring borehole further constrains uncertainty by capturing subsurface dynamics between the injector and producer. These findings underscore the importance of monitoring pressure and temperature profiles in the wells of a geothermal doublet.
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Long-term geothermal production is subject to considerable uncertainty due to limited data availability and inherent geological heterogeneity. While observation and data acquisition improve our understanding of the reservoir, they also contribute significantly to project costs. It is essential to identify the most informative observation strategy. In this study, we apply a previously developed scenario-based data assimilation framework that integrates rapid geological modelling, efficient numerical simulation, and Ensemble Smoother with Multiple Data Assimilation (ESMDA) to constrain uncertainties in reservoir properties and production forecasts to a synthetic but geologically realistic fluvial geothermal system and conduct a data worth analysis to evaluate the impact of different observations (production temperature and injection pressure, well temperature and pressure profiles, etc.) on uncertainty reduction. Results show that production temperature and injection pressure alone, though cost-effective, are insufficient to significantly reduce uncertainties in reservoir performance forecasts. In contrast, well temperature and pressure profiles exhibit substantially higher data worth, leading to much better-constrained predictions. Moreover, incorporating a monitoring borehole further constrains uncertainty by capturing subsurface dynamics between the injector and producer. These findings underscore the importance of monitoring pressure and temperature profiles in the wells of a geothermal doublet.
Hydrogen Flow and Trapping in Sandstone Rocks
Comparing Pore-Scale Experiments with Pore Network Modelling
Understanding pore-scale hydrogen displacement and trapping is crucial for developing subsurface hydrogen storage facilities. While pore-scale flow visualization experiments provide critical insights, they are complex and re source-intensive. Quasi-static pore-network models (PNMs) offer a faster alternative for simulating multiphase flow. This study uses a widely employed PNM to simulate hydrogen flow in sandstones, comparing results with pore-scale flow visualization experiments at reservoir conditions.
Two sandstone samples were used: homogeneous Bentheimer and heterogeneous Clashach. Pore networks were extracted comprising pores and throats, and hydrogen-water flow was simulated, modelling drainage and imbibition processes. Results were analysed for fluid saturations and pore occupancies.
For the homogeneous rock, the PNM matches experimental results for both drainage and imbibition, enabling simulations of different wettability conditions and multiple injection and production cycles. For the heterogeneous rock, the PNM reasonably predicts the hydrogen flow path during drainage but fails to accurately predict imbibition. This discrepancy highlights the limitations of PNMs in predicting pore-scale flow in complex rocks.
In conclusion, while PNMs offer a computationally efficient means to simulate hydrogen flow, they cannot currently replace experimental observations for complex rocks. Further validation against experimental findings is necessary to refine these models and expand their applicability for underground hydrogen storage. ...
Two sandstone samples were used: homogeneous Bentheimer and heterogeneous Clashach. Pore networks were extracted comprising pores and throats, and hydrogen-water flow was simulated, modelling drainage and imbibition processes. Results were analysed for fluid saturations and pore occupancies.
For the homogeneous rock, the PNM matches experimental results for both drainage and imbibition, enabling simulations of different wettability conditions and multiple injection and production cycles. For the heterogeneous rock, the PNM reasonably predicts the hydrogen flow path during drainage but fails to accurately predict imbibition. This discrepancy highlights the limitations of PNMs in predicting pore-scale flow in complex rocks.
In conclusion, while PNMs offer a computationally efficient means to simulate hydrogen flow, they cannot currently replace experimental observations for complex rocks. Further validation against experimental findings is necessary to refine these models and expand their applicability for underground hydrogen storage. ...
Understanding pore-scale hydrogen displacement and trapping is crucial for developing subsurface hydrogen storage facilities. While pore-scale flow visualization experiments provide critical insights, they are complex and re source-intensive. Quasi-static pore-network models (PNMs) offer a faster alternative for simulating multiphase flow. This study uses a widely employed PNM to simulate hydrogen flow in sandstones, comparing results with pore-scale flow visualization experiments at reservoir conditions.
Two sandstone samples were used: homogeneous Bentheimer and heterogeneous Clashach. Pore networks were extracted comprising pores and throats, and hydrogen-water flow was simulated, modelling drainage and imbibition processes. Results were analysed for fluid saturations and pore occupancies.
For the homogeneous rock, the PNM matches experimental results for both drainage and imbibition, enabling simulations of different wettability conditions and multiple injection and production cycles. For the heterogeneous rock, the PNM reasonably predicts the hydrogen flow path during drainage but fails to accurately predict imbibition. This discrepancy highlights the limitations of PNMs in predicting pore-scale flow in complex rocks.
In conclusion, while PNMs offer a computationally efficient means to simulate hydrogen flow, they cannot currently replace experimental observations for complex rocks. Further validation against experimental findings is necessary to refine these models and expand their applicability for underground hydrogen storage.
Two sandstone samples were used: homogeneous Bentheimer and heterogeneous Clashach. Pore networks were extracted comprising pores and throats, and hydrogen-water flow was simulated, modelling drainage and imbibition processes. Results were analysed for fluid saturations and pore occupancies.
For the homogeneous rock, the PNM matches experimental results for both drainage and imbibition, enabling simulations of different wettability conditions and multiple injection and production cycles. For the heterogeneous rock, the PNM reasonably predicts the hydrogen flow path during drainage but fails to accurately predict imbibition. This discrepancy highlights the limitations of PNMs in predicting pore-scale flow in complex rocks.
In conclusion, while PNMs offer a computationally efficient means to simulate hydrogen flow, they cannot currently replace experimental observations for complex rocks. Further validation against experimental findings is necessary to refine these models and expand their applicability for underground hydrogen storage.
Conference paper
(2025)
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Guofeng Song, Sebastian Geiger, Denis Voskov, Hemmo A. Abels, Philip J. Vardon
Efficient geothermal resource development remains challenging due to inherent geological uncertainty and limited subsurface data. A proof-of-concept for a digital twin for a fluvial geothermal reservoir, similar to the Delft campus geothermal project, is presented. This digital twin has the aim to integrate geological scenario modeling, production simulation, uncertainty analysis, and data assimilation to mitigate operational risks, reduce maintenance costs, extend reservoir longevity, and enhance the overall sustainability of this project. In this contribution, we assess the efficiency of the ensemble smoother with multiple data assimilation (ESMDA) for subsurface property inversion of a fluvial geothermal system. First, we developed an efficient method that allows for the swift creation of multiple geological scenarios of channelized reservoir geometries, fully constrained to well information, using Rapid Reservoir Modeling (RRM). Next, we generated an ensemble containing multiple geological realizations for a given scenario representing the geothermal system using stochastic reservoir modelling. For a single scenario and its ensemble of stochastically generated property distributions, heat flow and production rates were simulated using the Delft Advanced Research Terra Simulator (DARTS). One of the ensemble members and its simulated production data were taken as the “truth” (or reference) case. ESMDA was then employed to invert the property distribution within the fluvial channels of all other ensemble members, using the “observed” temperature and pressure data along the injection and production well from the “truth” case. We also consider the presence of a monitoring borehole to analyze how additional monitoring data impacts the convergence of ESMDA. The simulation results of the posterior models demonstrated a significant reduction in root mean square error for temperature and pressure data which align more closely with the “observations” compared to the prior models. This outcome confirms the feasibility of applying ESMDA for data assimilation in fluvial geothermal systems, such as the Delft campus geothermal project.
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Efficient geothermal resource development remains challenging due to inherent geological uncertainty and limited subsurface data. A proof-of-concept for a digital twin for a fluvial geothermal reservoir, similar to the Delft campus geothermal project, is presented. This digital twin has the aim to integrate geological scenario modeling, production simulation, uncertainty analysis, and data assimilation to mitigate operational risks, reduce maintenance costs, extend reservoir longevity, and enhance the overall sustainability of this project. In this contribution, we assess the efficiency of the ensemble smoother with multiple data assimilation (ESMDA) for subsurface property inversion of a fluvial geothermal system. First, we developed an efficient method that allows for the swift creation of multiple geological scenarios of channelized reservoir geometries, fully constrained to well information, using Rapid Reservoir Modeling (RRM). Next, we generated an ensemble containing multiple geological realizations for a given scenario representing the geothermal system using stochastic reservoir modelling. For a single scenario and its ensemble of stochastically generated property distributions, heat flow and production rates were simulated using the Delft Advanced Research Terra Simulator (DARTS). One of the ensemble members and its simulated production data were taken as the “truth” (or reference) case. ESMDA was then employed to invert the property distribution within the fluvial channels of all other ensemble members, using the “observed” temperature and pressure data along the injection and production well from the “truth” case. We also consider the presence of a monitoring borehole to analyze how additional monitoring data impacts the convergence of ESMDA. The simulation results of the posterior models demonstrated a significant reduction in root mean square error for temperature and pressure data which align more closely with the “observations” compared to the prior models. This outcome confirms the feasibility of applying ESMDA for data assimilation in fluvial geothermal systems, such as the Delft campus geothermal project.
Evaluating large-scale saline aquifers
Unlocking CO 2 storage in the Santos Basin through consistent multiscale analysis
Regional-scale saline aquifers are promising candidates for geological CO2 storage but present significant modeling challenges due to their vast extent, heterogeneity, and limited subsurface data. This study introduces a multiscale modeling framework that was applied to assess CO2 storage in the Ponta Aguda saline aquifer (Santos Basin, Brazil, 40,000 km2 area). Consistency of the multiscale models is checked by combining boundaries conditions for pressure match and verification of trapping mechanisms representativity. Four different methods were evaluated regarding the trapping mechanisms accuracy in coarse models: Local Grid Refinement, Effective Values, and Algebraic Dynamic Multilevel. Compositional simulations conducted with CMG-GEM and DARSim2 demonstrate that coarse-scale models systematically overestimate CO2 trapping due to numerical artifacts, particularly in solubility and hysteresis behavior. These artifacts arise from mismatched CO2/brine volumes in large cells, leading to artificially enhanced trapping efficiency. Among the evaluated methods, Algebraic Dynamic Multilevel delivers the most reliable predictions, providing a general solution that aligns closely with fine-scale reference simulations while remaining computationally feasible. The results show the importance of scale-consistent modeling approaches for accurate CO2 storage assessment and highlight the risks of relying on overly simplified coarse models in the design and optimization of carbon storage projects in large saline aquifers.
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Regional-scale saline aquifers are promising candidates for geological CO2 storage but present significant modeling challenges due to their vast extent, heterogeneity, and limited subsurface data. This study introduces a multiscale modeling framework that was applied to assess CO2 storage in the Ponta Aguda saline aquifer (Santos Basin, Brazil, 40,000 km2 area). Consistency of the multiscale models is checked by combining boundaries conditions for pressure match and verification of trapping mechanisms representativity. Four different methods were evaluated regarding the trapping mechanisms accuracy in coarse models: Local Grid Refinement, Effective Values, and Algebraic Dynamic Multilevel. Compositional simulations conducted with CMG-GEM and DARSim2 demonstrate that coarse-scale models systematically overestimate CO2 trapping due to numerical artifacts, particularly in solubility and hysteresis behavior. These artifacts arise from mismatched CO2/brine volumes in large cells, leading to artificially enhanced trapping efficiency. Among the evaluated methods, Algebraic Dynamic Multilevel delivers the most reliable predictions, providing a general solution that aligns closely with fine-scale reference simulations while remaining computationally feasible. The results show the importance of scale-consistent modeling approaches for accurate CO2 storage assessment and highlight the risks of relying on overly simplified coarse models in the design and optimization of carbon storage projects in large saline aquifers.
Conference paper
(2025)
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Q. Zhang, S. Geiger, J. Storms, H. Hajibeygi, M. Jackson, G. Hampson, C. Jacquemyn, S. Krevor, A. Martinius
The North Sea’s potential as a Green Energy Hub depends on large-scale CO2 storage in shallow-marine sandstones, but the effects of geologic heterogeneity, such as permeability barriers and capillary entry pressure contrasts, remain underexplored. This study uses multiphase flow simulations on geologically realistic, surface-based reservoir models informed by outcrop analogue data from wave-dominated shoreface sandstones. We investigate how sedimentological heterogeneity influences CO2 plume migration, pressure evolution, and storage capacity.
Preliminary results show that capillary barriers tied to facies architecture and early cementation, conditioned to clinoform geometries, significantly control plume movement. These barriers promote lateral spreading and residual trapping, representing a potential upper limit on long-term CO2 storage when stable. Clinoform-related heterogeneity also induces flow compartmentalization, limiting pressure dissipation and enhancing anisotropy, which may reduce injectivity and cause spatially variable pressure buildup.
Comparisons with waterflood simulations reveal contrasting dynamics: water advances more uniformly, while CO2 migration is more sensitive to fine-scale architecture due to its lower interfacial tension and capillary entry pressures. These findings underscore the need to incorporate realistic sedimentological heterogeneity in dynamic models to avoid misestimating injectivity, pressure behavior, and storage security. This approach offers a robust framework for early-stage screening and risk assessment in complex storage settings. ...
Preliminary results show that capillary barriers tied to facies architecture and early cementation, conditioned to clinoform geometries, significantly control plume movement. These barriers promote lateral spreading and residual trapping, representing a potential upper limit on long-term CO2 storage when stable. Clinoform-related heterogeneity also induces flow compartmentalization, limiting pressure dissipation and enhancing anisotropy, which may reduce injectivity and cause spatially variable pressure buildup.
Comparisons with waterflood simulations reveal contrasting dynamics: water advances more uniformly, while CO2 migration is more sensitive to fine-scale architecture due to its lower interfacial tension and capillary entry pressures. These findings underscore the need to incorporate realistic sedimentological heterogeneity in dynamic models to avoid misestimating injectivity, pressure behavior, and storage security. This approach offers a robust framework for early-stage screening and risk assessment in complex storage settings. ...
The North Sea’s potential as a Green Energy Hub depends on large-scale CO2 storage in shallow-marine sandstones, but the effects of geologic heterogeneity, such as permeability barriers and capillary entry pressure contrasts, remain underexplored. This study uses multiphase flow simulations on geologically realistic, surface-based reservoir models informed by outcrop analogue data from wave-dominated shoreface sandstones. We investigate how sedimentological heterogeneity influences CO2 plume migration, pressure evolution, and storage capacity.
Preliminary results show that capillary barriers tied to facies architecture and early cementation, conditioned to clinoform geometries, significantly control plume movement. These barriers promote lateral spreading and residual trapping, representing a potential upper limit on long-term CO2 storage when stable. Clinoform-related heterogeneity also induces flow compartmentalization, limiting pressure dissipation and enhancing anisotropy, which may reduce injectivity and cause spatially variable pressure buildup.
Comparisons with waterflood simulations reveal contrasting dynamics: water advances more uniformly, while CO2 migration is more sensitive to fine-scale architecture due to its lower interfacial tension and capillary entry pressures. These findings underscore the need to incorporate realistic sedimentological heterogeneity in dynamic models to avoid misestimating injectivity, pressure behavior, and storage security. This approach offers a robust framework for early-stage screening and risk assessment in complex storage settings.
Preliminary results show that capillary barriers tied to facies architecture and early cementation, conditioned to clinoform geometries, significantly control plume movement. These barriers promote lateral spreading and residual trapping, representing a potential upper limit on long-term CO2 storage when stable. Clinoform-related heterogeneity also induces flow compartmentalization, limiting pressure dissipation and enhancing anisotropy, which may reduce injectivity and cause spatially variable pressure buildup.
Comparisons with waterflood simulations reveal contrasting dynamics: water advances more uniformly, while CO2 migration is more sensitive to fine-scale architecture due to its lower interfacial tension and capillary entry pressures. These findings underscore the need to incorporate realistic sedimentological heterogeneity in dynamic models to avoid misestimating injectivity, pressure behavior, and storage security. This approach offers a robust framework for early-stage screening and risk assessment in complex storage settings.
Capillary pinning in sedimentary rocks for CO2 storage
Mechanisms, terminology and State-of-the-Art
Review
(2025)
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Qin Zhang, Sebastian Geiger, Joep E.A. Storms, Denis V. Voskov, Matthew D. Jackson, Gary J. Hampson, Carl Jacquemyn, Allard W. Martinius
Capillary pinning refers to the immobilization of CO₂ at capillary barriers when the uprising CO2 pressure is lower than the capillary entry pressure of the overlaying pore throats. Also known as local capillary trapping, it has been proposed as a fifth geologic CO₂ storage mechanism, alongside structural, solubility, residual, and mineral trapping. Despite extensive research, the fragmented terminology surrounding capillary pinning has led to confusion, making it challenging to synthesize findings effectively. Often conflated with mechanisms such as residual and hysteresis trapping, capillary pinning is commonly underestimated or completely overlooked in reservoir-scale models. Furthermore, difficulties in characterizing and upscaling small-scale geologic heterogeneities that influence capillary pinning contribute to significant uncertainties, with estimates of CO₂ trapped via this mechanism ranging from 3 % to 100 % of total CO₂ trapped via capillary actions. This review explores the fundamental mechanisms, experimental findings, and modeling approaches for assessing CO₂ capillary pinning in carbon capture and storage (CCS). It seeks to bridge the gap between the reservoir engineering community, with its extensive expertise in hydrocarbon recovery but that needs adjustments for CCS applications, and the subsurface storage community, which stands to benefit from this knowledge but often lacks access to relevant literature. Additionally, the study identifies key research opportunities to advance the understanding of capillary pinning in sedimentary rocks, ultimately enhancing the efficacy and reliability of CCS operations.
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Capillary pinning refers to the immobilization of CO₂ at capillary barriers when the uprising CO2 pressure is lower than the capillary entry pressure of the overlaying pore throats. Also known as local capillary trapping, it has been proposed as a fifth geologic CO₂ storage mechanism, alongside structural, solubility, residual, and mineral trapping. Despite extensive research, the fragmented terminology surrounding capillary pinning has led to confusion, making it challenging to synthesize findings effectively. Often conflated with mechanisms such as residual and hysteresis trapping, capillary pinning is commonly underestimated or completely overlooked in reservoir-scale models. Furthermore, difficulties in characterizing and upscaling small-scale geologic heterogeneities that influence capillary pinning contribute to significant uncertainties, with estimates of CO₂ trapped via this mechanism ranging from 3 % to 100 % of total CO₂ trapped via capillary actions. This review explores the fundamental mechanisms, experimental findings, and modeling approaches for assessing CO₂ capillary pinning in carbon capture and storage (CCS). It seeks to bridge the gap between the reservoir engineering community, with its extensive expertise in hydrocarbon recovery but that needs adjustments for CCS applications, and the subsurface storage community, which stands to benefit from this knowledge but often lacks access to relevant literature. Additionally, the study identifies key research opportunities to advance the understanding of capillary pinning in sedimentary rocks, ultimately enhancing the efficacy and reliability of CCS operations.
CO2 Storage Complex in Santos Basin, Brazil
Storage Potential and Impacts of Heterogeneity in Pressure Front
Conference paper
(2025)
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F. Silva Lira, M. Erdtmann, G. Gantois, F. Da Costa, H. Zerfass, L. Cassino, P. Walter, A. Martinius, S. Geiger, L. Menezes, S. Bortolini, A. Guirro, G. Waisman, F. Bulhoes, G. Freitas, G. Vieira, N. Lima
Brazil’s industrial emissions are 180 million tons of CO2 per year, and approximately 60% of these emissions are coming from industrial clusters located in the southeast. Tthe development of new offshore storage locations in this region is hence of strategic importance for future Carbon Capture and Storage (CCS) projects in Brazil. This study presents an evaluation of CO2 storage in the deep saline aquifers of the Jureia-Ponta Aguda formation, a gigaton-scale storage resource located in the shallow waters of Santos Basin that has the potential to support the development of at least three larger CCS hubs, each with a target injection of 1Gt of CO2. We show how Lorenz Coefficient map allows us to screen the pressure influence areas for each hub by linking reservoir heterogeneity to the spatio-temporal evolution of the pressure front, thereby identifying potential risks of pressure interference between neighboring CCS hubs.
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Brazil’s industrial emissions are 180 million tons of CO2 per year, and approximately 60% of these emissions are coming from industrial clusters located in the southeast. Tthe development of new offshore storage locations in this region is hence of strategic importance for future Carbon Capture and Storage (CCS) projects in Brazil. This study presents an evaluation of CO2 storage in the deep saline aquifers of the Jureia-Ponta Aguda formation, a gigaton-scale storage resource located in the shallow waters of Santos Basin that has the potential to support the development of at least three larger CCS hubs, each with a target injection of 1Gt of CO2. We show how Lorenz Coefficient map allows us to screen the pressure influence areas for each hub by linking reservoir heterogeneity to the spatio-temporal evolution of the pressure front, thereby identifying potential risks of pressure interference between neighboring CCS hubs.