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.

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.

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.

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.

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.

Net-transgressive, shallow-marine sandstone reservoirs overlain by thick mudstone seals are prime candidates for storage of CO2, H2 and thermal energy. Although these reservoirs have high net-to-gross ratios, analogous outcrops demonstrate a wide range of sedimentological heterogeneities that are sampled only sparsely or at low resolution in subsurface data. We use a combination of outcrop data, sketch-based reservoir modelling and flow diagnostics to assess the impact of sedimentological heterogeneities on subsurface storage.

The Cliff House Sandstone outcrop example comprises wave-dominated shoreface sandstones arranged in aggradationally-to-retrogradationally stacked parasequences, which overlie and pass up depositional dip into mudstone-dominated coastal plain, lagoonal and tidal flat deposits that encase channelized tidal and tidally influenced fluvial sandbodies. Reservoir models of this outcrop example demonstrate that effective horizontal permeability, flow patterns and displacement, and stratigraphic trapping potential are controlled by: (1) the packaging of shoreface sandstones into laterally extensive parasequences bounded by offshore mudstones; (2) the spatial distribution, connectivity and permeability of channelized sandbodies; and (3) the localized connections between channelized sandbodies and shoreface sandstones. The last two parameters are likely to be poorly constrained in subsurface seismic and well data, and their potential effects require evaluation in reservoir modelling studies.

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.

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.

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.

Fluvial reservoirs are a major target for geothermal energy production. Interpreting the 3D reservoir architectures from 2D seismic datasets, which usually are acquired for geothermal systems, is difficult. In particular, small-scale geological factors like sandbody connectivity are challenging to resolve. This study addresses these issues through a novel workflow that incorporates 3D geological and 2D seismic modelling methods to assess the seismic responses of stratigraphic attributes in fluvial geothermal reservoirs where data availability is low.

Two synthetic fluvial reservoir scenarios were built, ranging from a single channelised deposit to a geologically more plausible model ensemble of fluvial deposits, which represents the reservoir heterogeneities that could be present at the geothermal doublet at Delft University of Technology. Acoustic finite-difference modelling was combined with seismic imaging to create 2D depth images. Our results reveal how seismic resolution determines our ability to correctly identify sandbody connectivity and capture inner channel details. Whereas channel bodies can be detected, the best frequency spectra for observing certain geological features remain unclear. These findings emphasise that quantitative multi-scale analysis, advanced imaging techniques, and survey design optimisation are central to improving seismic characterisation of fluvial geothermal systems in future research.

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.

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.

Achieving climate neutrality requires rapid scale-up of CO2 storage to gigatonne scale. Storage clusters—multiple injection sites sharing regional aquifers—offer economic benefits but introduce new challenges in subsurface pressure management. Elevated reservoir pressures can lead to fault slip and leakage, generating environmental and operational risks that span beyond individual license areas. Current site-focused workflows are insufficient for characterizing such cross-boundary effects.

This work introduces the research activities and key ideas of the international research project MuPSI which develops an integrated, multiscale screening and simulation approach to assess geomechanical risks in storage clusters. We present results of a new screening workflow that enables rapid evaluation of pressure interference and fault activation risk across regional aquifers. This is coupled with high-resolution modeling of fault response and new software to bridge region-, project-, and fault-scales. A new highly efficient approach for pressure-stress coupling offers greater software flexibility in geomechanical assessment of individual projects.

The approach is demonstrated using North Sea case studies, including the Horda Platform (Norway) and East Mey (UK). Outputs will support operators and regulators in improving investment decisions, permitting, and cross-license coordination. MuPSI also delivers stakeholder training and knowledge-transfer tools to accelerate adoption of robust, risk-informed storage cluster design.

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.

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.

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

Understanding effective permeability is crucial for predicting fluid migration and trapping in subsurface reservoirs. The Bunter Sandstone of northwestern Europe hosts major groundwater and geothermal resources and is targeted for CO2 storage projects. Here the effective permeability of fluvial facies within the Bunter Sandstone Formation was assessed using facies-scale models. Twelve lithofacies were modeled based on core and outcrop observations of their geometries and dimensions. Permeability values from minipermeameter measurements were assigned to low- and high-permeability lithologies in each facies. The dimensions of a Representative Elementary Volume (REV) in depositional dip, depositional strike and vertical directions were determined by extracting sub-volumes from the models at different scales, calculating values of effective permeability for each sub-volume, and identifying the sub-volume at which the values of effective permeability stabilise as the REV. The REV dimensions vary with facies type and flow direction, but are typically of order tens of centimetres to metres in size, significantly larger than a typical core plug. Having identified the REV, we analyze the effective permeabilities of the different facies types. Normalized values of effective permeabilities in depositional dip, strike and vertical directions (kd, ks, kv), relative to the permeability of low- and high-permeability lithologies in each facies, display a positive linear correlation with the proportion of high-permeability lithology (clay-poor sandstone) for all facies. Therefore, the proportion of clay-poor sandstone, as measured in core data, can be used to predict facies-scale effective permeability in the Bunter Sandstone Formation, as well as in analogous fluvial deposits globally.

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.

This study evaluates the geothermal potential in an Area of Interest (AOI) in the southeast of Gran Canaria, focusing on location selection near the rift zone and NW-SE vertical fracture zones.

Via a 3D resistivity model nine conductive bodies were identified in the AOI. Then interpretations of the location of these bodies were constructed based on magnetetelluric (MT), density, and S-wave velocity data with geochemical analyses of gas emissions, groundwater chemistry, temperature gradients and the geological history of the AOI. Eventually the geothermal potential of these locations within the AOI was assessed via six criteria: degree of hydrothermal alteration, depth, hydrothermal activity, marine intrusion, top-down area size and fracture density.

Finally a conceptual geological model of the most promising location was made with a sub-vertical fracture system. Several scenarios were tested as part of a sensitivity analysis, all of which are plausible and therefore not irrelevant. In these scenarios key parameters such as porosity, permeability, geothermal gradients, and the permeability ratio (kz/kx/ky) within the fracture zone were varied. One of the main findings was that the 10/10/1 permeability ratio, considered the most realistic for sub-vertical fractures, showed minimal impact on production performance.

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.