This session invites contributions advancing data-driven, physics-informed, and hybrid physics–ML methodologies for geothermal, geotechnical, and geoenvironmental applications relevant to energy and subsurface resources. Topics include subsurface and site characterization, reservoir engineering, subsurface flow and transport, induced seismicity, coupled thermo-hydro-mechanical-chemical processes, as well as geotechnical aspects of geothermal infrastructure such as foundations, tunnelling, and slope stability. Contributions employing supervised and unsupervised learning, deep learning, physics-informed neural networks, surrogate and reduced-order models, and inverse modelling approaches are particularly encouraged. We also welcome contributions that integrate laboratory and field experimentation, such as CT imaging, NMR, scattering methods, core- and rock-mechanical testing, and field monitoring, with data science and physics-informed modelling workflows for parameter inference, model calibration, and validation.
A central focus of the session is digital innovation for geothermal energy systems, spanning exploration, development, monitoring, and operation. Relevant topics include geothermal and subsurface databases; data quality control, uncertainty quantification, and metadata standards; integration of multi-source datasets (geological, geophysical, thermal, geochemical, and operational); and AI/ML approaches for resource assessment, reservoir characterisation and modelling, performance forecasting, and operational risk or failure prediction. Contributions related to open and FAIR data infrastructures, semantic technologies, and European and national initiatives (e.g., GeoERA, DESTRESS, HeatStore, GSEU, GeoMAP, MALEG, WärmeGut) are particularly welcome.
The session also welcomes studies integrating ML and data science with monitoring technologies such as IoT sensor networks, remote sensing, and real-time data streams, as well as the development of digital twins for geothermal and subsurface energy systems.
Orals: Fri, 8 May, 08:30–12:30 | Room -2.43
Understanding and predicting complex environmental and hydrosystem processes is a central challenge in Earth system science. These systems are governed by interacting physical mechanisms across scales, are only partially observed, and are often characterized by limited data and substantial uncertainty. As a result, machine learning (ML) has emerged along two complementary development paths for environmental modeling.
In a first branch, physical process models remain the backbone of simulation, while ML is employed as a surrogate to approximate expensive numerical solvers. Surrogate modeling approaches based on Gaussian process emulators, polynomial chaos expansions, support vector regression, and related probabilistic representations are particularly well suited for data-poor settings. Neural networks are used more selectively in this context, as uncertainty-aware and sample-efficient methods are often preferred. In surrogate modeling, considerable effort is devoted to optimal sampling strategies, including active learning, which adaptively select informative simulations and help preserve scarce computational resources. These surrogate models enable efficient uncertainty quantification, sensitivity analysis, and Bayesian inference, while preserving physical interpretability.
A second, increasingly important branch emerges when physical models are incomplete, unavailable, or deliberately omitted, and ML models replace the governing equations altogether. This branch is most commonly based on neural network representations, but has recently also been explored using Gaussian processes and polynomial chaos–based learning concepts. In this setting, purely data-driven learning is insufficient, as unconstrained models tend to violate physical principles and extrapolate poorly. In this second branch, physical principles such as conservation laws or balance relations are embedded directly into learning architectures. Complementarily, constraint-driven learning strategies enforce physical laws, admissibility conditions, and structural consistency during training. By restricting the hypothesis space, these methods stabilize learning and support robust inference under incomplete physical knowledge.
Taken together, surrogate modeling for physics-based simulations and physics-aware ML for equation-free learning represent two coherent and complementary branches of modern environmental machine learning. We observe a growing convergence between these two branches, as physics-based surrogate modeling and equation-free machine learning increasingly borrow concepts from each other. This convergence is not accidental, but a direct response to fundamental model limitations and the challenge of making reliable predictions under scarce data and knowledge constraints. By integrating physics, probabilistic reasoning and constraints, emerging approaches increasingly focus on robustness and interpretability rather than unconstrained expressive power.
How to cite: Oladyshkin, S.: Physics-Aware Learning for Environmental Systems: Surrogate Modeling and Constraint-Driven Machine Intelligence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3804, https://doi.org/10.5194/egusphere-egu26-3804, 2026.
Physics-Informed Neural Networks (PINNs) have emerged as a promising paradigm for solving problems governed by partial differential equations (PDEs) using the flexibility and generalization capability of deep learning. By embedding the governing physical laws directly into the training process, PINNs can approximate complex physical systems even when limited or no observational data are available. However, their performance and convergence can deteriorate significantly in domains characterized by high heterogeneity or discontinuities in material properties. In particular, standard PINN formulations tend to enforce implicit continuity in the hydraulic conductivity field, which can lead to inaccurate representations of physical processes in heterogeneous porous media.
This study introduces a novel and robust PINN framework for modelling transient fluid flow in heterogeneous porous media, with specific emphasis on accurately handling discontinuities in the hydraulic conductivity field. The proposed approach is based on a mixed formulation of the governing flow equations, in which the pressure and velocity fields are represented by independent neural networks. This structural separation eliminates the need to compute spatial derivatives of discontinuous or non-differentiable quantities during the evaluation of the loss function. As a result, the method achieves a more stable and accurate application of automatic differentiation while maintaining strong adherence to the underlying physical principles.
Furthermore, to address the high computational cost typically associated with training PINNs, a discrete-time mixed formulation is developed. By discretizing the temporal domain, this approach reduces the dimensionality of the problem, leading to substantial savings in both memory usage and training time. Despite these efficiency gains, the discrete-time PINN retains a high level of accuracy and fidelity in predicting transient flow dynamics in heterogeneous domains.
Comprehensive testing on various scenarios of unconfined aquifers demonstrate that the proposed implementation outperforms standard PINN approaches when applied to porous media with strong contrasts in hydraulic conductivity. The results obtained from the different PINNs techniques have been compared against the results from finite element software COMSOL to analyze their performance.
Overall, the mixed formulation PINN frameworks are computationally more efficient, and produce results with improved accuracy compared to the standard PINNs technique for simulating fluid flow in complex porous media systems, representing a significant step forward in the application of deep learning to subsurface modelling.
How to cite: Virupaksha, A. G., Fahs, M., Nagel, T., Lehmann, F., and Hotiet, H.: Physics informed neural networks based on mixed pressure-velocity formulation for flow in heterogeneous aquifers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1394, https://doi.org/10.5194/egusphere-egu26-1394, 2026.
Reactive nitrate transport in groundwater is governed by coupled advection–dispersion–reaction (ADR) dynamics and kinetically limited redox processes, including donor limitation and competition among electron acceptors. We compare two surrogate modeling approaches for reactive nitrate transport. The first is a physics-audited, data-driven approach based on a categorial boosting algorithm, with physical admissibility (e.g., non-negativity and ADR-consistent behavior) assessed via post-hoc diagnostics. The second is a physics-informed neural network (PINN) surrogate that embeds the ADR equation, boundary conditions, non-negativity, and a redox-ordering constraint directly into the training objective to promote mechanistic consistency. Both surrogates are trained and tested on the same one-dimensional PHREEQC benchmark suite spanning increasing hydrogeochemical complexity: linear denitrification, dual-linear nitrate–Fe(III) competition, dual-substrate Monod kinetics, and fully coupled dual-Monod redox systems. Predictive uncertainty is quantified to provide calibrated confidence bounds and identify regions of elevated sensitivity.
Results show that while both surrogates can interpolate reactive nitrate dynamics within the training domain, the PINN surrogate consistently provides superior physical consistency and robustness under increasing kinetic nonlinearity. Uncertainty estimates from the PINN are well calibrated, with prediction-interval widths increasing systematically near migrating reactive fronts where nonlinear redox competition amplifies model sensitivity. The results demonstrate that embedding governing physics directly into the learning process yields a more reliable and interpretable surrogate for uncertainty-aware reactive transport modeling, particularly in regimes dominated by nonlinear kinetics and competing redox pathways.
How to cite: Arab, A., Scheytt, T., Nagel, T., and Taherdangkoo, R.: From Data-Driven to Physics-Informed Surrogate Models for Reactive Nitrate Transport, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11359, https://doi.org/10.5194/egusphere-egu26-11359, 2026.
Generative super-resolution (SR) reconstruction models are widely applied in digital rock research to balance the
trade-off between image resolution and the scanning device’s field of view. Existing methods often enhance
visual details or structural fidelity separately. However, they fail to balance these goals effectively. This failure
frequently leads to artifacts that distort porosity and permeability measurements. This paper proposes the Sta
tionary and Discrete Wavelet-Enhanced Generative Adversarial Network (SDWGAN). The model is a hybrid SR
approach that integrates two wavelet decomposition methods. This integration addresses the challenge effec
tively. By integrating multi-scale frequency constraints from wavelet decomposition with adversarial training
focused on high-frequency components, our method effectively distinguishes rock boundary details from imaging
artifacts. The proposed model adopts a global-local feature integration architecture to preserve fine-grained
textures and macroscopic structures. Experimental results on the DeepRock-SR dataset (carbonate, sandstone,
coal) demonstrate SDWGAN’s enhancements: 0.63–2.12 dB PSNR and 0.01–0.11 SSIM improvements in fidelity,
alongside 0.001–0.005 LPIPS and 0.62 NIQE gains in perceptual quality over RGB-domain loss-based models.
Simulated seepage results indicate that SDWGAN estimates porosity and permeability with 98 % similarity to the
reference images. In conclusion, the proposed model manages the perception-distortion trade-off via frequency
domain optimization, ensuring petrophysical consistency between SR results and benchmarks. This approach
offers a novel and reliable method for reservoir characterization in the field of petroleum geology.
How to cite: Ma, G. and Wang, Z.: Multiscale wavelet-adversarial learning eliminates imaging artifacts in digital rock analysis for reliable reservoir evaluation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-576, https://doi.org/10.5194/egusphere-egu26-576, 2026.
This study evaluates the field applicability of a multi-resolution Convolutional Long Short-Term Memory (ConvLSTM) framework for predicting time-series retaining wall deformations during staged excavation using field measurements from various excavation sites in South Korea. The proposed framework integrates three ConvLSTM models trained with different temporal input resolutions to capture deformation characteristics at multiple time scales. Their multi-step predictions are subsequently refined using a stacking ensemble strategy with a neural network–based meta-learner, which mitigates error accumulation commonly observed in recursive long-horizon forecasting and enhances overall prediction stability and accuracy.
To generate a comprehensive training database, numerical analyses were conducted on a wide range of excavation cross-sections with varying final excavation depths, wall tip restraint conditions, and initial groundwater levels, reflecting diverse geotechnical and structural configurations. The geotechnical and structural properties were defined probabilistically to account for inherent uncertainties in ground conditions and structural stiffness. In total, 4,000 numerical analysis cases were generated and further augmented into 16,000 training datasets through Gaussian noise injection to improve model generalization ability.
For field validation, 34 time-series displacement measurements collected from 11 excavation sites in South Korea were employed to assess the predictive performance of the proposed framework under real construction conditions. When lateral displacement data obtained from earlier excavation stages were provided as inputs, the model predicted retaining wall deformation induced by an additional excavation depth of 5.0 m, achieving an average coefficient of determination (R²) of 0.85 and a mean absolute error (MAE) of 5 mm. Furthermore, the framework demonstrated an average inference time of 0.92 s, confirming its suitability for near–real-time prediction and potential integration with field monitoring systems. These results indicate that the proposed multi-resolution ensemble framework is practically applicable to real-world excavation projects and offers a robust tool for predictive decision-making in excavation safety management.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2023R1A2C1007635).
How to cite: Kim, J. and Youn, H.: AI-Driven Time-Series Prediction of Retaining Wall Deformation: A Case Study in Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3675, https://doi.org/10.5194/egusphere-egu26-3675, 2026.
Hydraulic conductivity of fractured rock masses is a controlling parameter in dam engineering, governing seepage and grouting performance. In practice, hydraulic conductivity is commonly evaluated using in-situ packer (Lugeon) or commonly known hydro-jacking tests. However, these tests are costly, time consuming and cumbersome, requiring skilled technical staff. Therefore, empirical models are often used to estimate the hydraulic conductivity, which generally rely on a limited number of input variables, and therefore inadequately represent the nonlinear permeability behavior of rock masses. To address these limitations, this study proposes a machine-learning based modeling framework for predicting hydraulic conductivity of fractured rock masses using published data comprising packer test results, hydro-jacking reopening pressures, and geological parameters including depth, rock quality designation (RQD), and fracture characteristics. Hydro-jacking tests are performed at the Mohmand dam site, and the model performance is evaluated against the test data. The results indicate that the machine-learning based model is reliable and can accurately capture hydraulic conductivity in fractured rock masses. The proposed approach offers a reliable alternative to traditional empirical methods and has practical implications for seepage assessment, grouting design, and dam foundation permeability evaluation in complex geological settings.
How to cite: Zarrar, Z., Ali, Z., Vaseer, Z., Saeed, S., and Khan, I.: Predicting Hydraulic Conductivity of Rock Masses using Machine Learning and Hydro-Jacking Tests – A Case Study of Mohmand Dam, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11794, https://doi.org/10.5194/egusphere-egu26-11794, 2026.
Geological carbon storage is a key strategy for mitigating global CO2 emissions, and reliable monitoring of subsurface CO2 migration is critical for storage safety. Time-lapse seismic provides valuable insights into CO2 plume evolution. However, accurately predicting high-resolution CO2 saturation from seismic data remains a major challenge. In this study, we propose a novel physics-constrained deep learning framework that treats time-lapse seismic data as video sequences and leverages the Video Masked Autoencoder (VideoMAE) architecture to capture spatial and temporal dependencies. The approach consists of two stages: self-supervised pretraining on seismic data and supervised fine-tuning for CO2 saturation prediction. During pretraining, masked reconstruction enables the model to extract rich spatiotemporal feature representations from seismic videos. In fine-tuning, the pretrained model is adapted to predict future CO2 saturation from historical time-lapse seismic data without requiring seismic data from the target year. A physical constraint based on Fick’s law of diffusion is incorporated into the loss function to regularize the temporal evolution of CO2 saturation during fine-tuning. Results on the Kimberlina synthetic multiphysics dataset demonstrate that the physics-constrained VideoMAE framework consistently outperforms baseline models in both prediction accuracy and spatial consistency. These findings highlight the effectiveness of combining video-based self-supervised learning with physical constraints for time-lapse seismic monitoring and provide a promising physics-informed approach for CO2 storage surveillance.
How to cite: Tang, M., Zong, Z., and Jiang, D.: CO2 Saturation Prediction from Historical Time-Lapse Seismic Data Using Physics-Constrained VideoMAE, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6206, https://doi.org/10.5194/egusphere-egu26-6206, 2026.
Accurate prediction of the hydraulic conductivity of compacted bentonite is critical for assessing the long-term safety of high-level radioactive waste repositories, where barrier efficiency depends on coupled processes. This study develops a data-driven machine learning model to predict saturated hydraulic conductivity and a physics-based machine learning model to predict unsaturated hydraulic conductivity of compacted bentonite. For the saturated hydraulic conductivity prediction, a dataset of 215 experimental measurements was compiled, incorporating key soil properties such as montmorillonite content, specific gravity, plasticity index, initial water content, dry density, and temperature as input. To predict unsaturated hydraulic conductivity, the study considers experimental data, synthetic data generated using the Van Genuchten model, and outputs from the machine learning model developed for saturated hydraulic conductivity. The input dataset includes specific gravity, montmorillonite content, initial dry density, initial water content, initial void ratio, plasticity index, and suction. The AdaBoost, CatBoost, and XGBoost algorithms were used to train the machine learning models, and the whale optimization algorithm was used for hyperparameter tuning. The trained machine learning models demonstrate good predictive performance for both saturated and unsaturated hydraulic conductivity of compacted bentonite, showing close agreement with experimental measurements.
How to cite: Shehab, M., Taherdangkoo, R., and Christoph, B.: Physics-based and data-driven machine learning modeling of saturated and unsaturated hydraulic conductivity of bentonite, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5372, https://doi.org/10.5194/egusphere-egu26-5372, 2026.
Shallow geothermal energy represents a significant opportunity to reduce energy waste in the heating and cooling sectors. Geothermal maps are a valuable tool for enhancing the exploitation of geothermal resources on a national scale. In this work, we produced maps of Italian shallow geothermal potential for different saturation and groundwater velocity scenarios. For this purpose, we developed a method for quickly computing geothermal potential, based on lithological data and the simplified application of the Moving Infinite Line Source model for heat dispersion. Our approach follows the G.POT methodology proposed by Casasso and Sethi in 2016, but it also incorporates the contribution of groundwater flow, which was not considered in the original G.POT computation. This method enables the computation of geothermal potential from a large amount of input data, given the geological asset, the thermal and hydrogeological properties of the materials that form the subsoil, the initial underground temperature, and the required thermal load. Using this approach, we estimated the geothermal potential related to more than 28.000 sites for which stratigraphic data are available. We gather the stratigraphic logs of every site and compute the geothermal potential for each lithological layer encountered in each log. The derived values have been averaged to obtain the mean potential of the shallow subsoil at a reference depth of approximately 70 m. The final maps are the result of interpolating the point estimates. The different scenarios explore the variability of the geothermal field as it is intrinsically linked to the geological and hydrogeological context. From completely unsaturated to completely saturated conditions, the geothermal potential can increase by a factor that ranges from 4 to 10, depending on the groundwater flow velocity. The regions showing larger increments related to groundwater action are those characterized by sandy or gravelly subsoils, such as Emilia-Romagna, Piedmont, Lombardy, Friuli-Venezia Giulia, and Veneto. The high permeability of these sediments strongly influences their geothermal potential. On the other hand, areas where consolidated rock prevails are less susceptible to variation due to the presence of water in the underground soils, as observed in some regions of Sardinia, Sicily, and Apulia. Both the final maps and selected intermediate results have been published on open-access data platforms managed by Ricerca sul Sistema Energetico - RSE S.p.A, which also host a wide range of other energy-related information to support territorial energy planning.
How to cite: Squarzoni, G., Colucci, F., and Aiello, M.: Mapping the geothermal potential of Italy’s shallow subsoil: a streamlined MILS model approach for extensive datasets, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3529, https://doi.org/10.5194/egusphere-egu26-3529, 2026.
The geothermal sector generates a vast amount of knowledge—from research project data to scientific publications, technical reports, patents, and open datasets—produced by scientists, operators, consultants, public authorities, and funding agencies. However, this wealth of information is often scattered across multiple repositories and platforms, which hampers effective access, integration, and utilization. EGRISE 2.0, developed within the EU-funded Geotherm-FORA project, addresses this challenge as the largest thematic repository for geothermal research and innovation in Europe. The platform aggregates information from EU-funded projects, open access publications, scientific journals, and public datasets hosted on repositories such as Zenodo and Pangaea. Each research product is indexed with detailed metadata, enabling users to search, filter, and explore thousands of documents—currently over 11,000—by criteria such as publication type, funder, country, year, language, or resource access.
By consolidating this vast body of knowledge and facilitating its exploration, EGRISE 2.0 allows stakeholders to precisely map the state of R&D in the geothermal industry. Researchers can spot emerging trends, identify gaps, and recognize key contributors, while funding agencies and policymakers can evaluate technological maturity and set priorities for future research and investment. Additionally, the platform facilitates the preparation of innovative project proposals by offering instant access to scientific publications, datasets, and project deliverables.
A set of integrated charts further enhances the platform’s value, offering insights such as publication trends, openness over time, and data FAIRness.
EGRISE is an open tool available at https://egrise.openaire.eu/. It is powered by OpenAIRE CONNECT, a service to build customizable search portals on top of the OpenAIRE Graph, one of the largest open scientific knowledge graph.
In this way, EGRISE 2.0 not only consolidates knowledge but actively empowers innovation, collaboration, and strategic decision-making leveraging on open research information, establishing itself as an indispensable tool for the European geothermal community.
How to cite: Trumpy, E., Bardi, A., and Manzella, A.: EGRISE 2.0: the knowledge at the fingertips to power Research and Innovation in Geothermal Energy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11715, https://doi.org/10.5194/egusphere-egu26-11715, 2026.
Former oil and gas fields offer a repository of historical geophysical well logs that can help support geothermal exploration across large areas. Lithology classification from logging data is a fundamental task in subsurface geological interpretation. Existing deep learning approaches typically formulate this problem as a point-wise or sequence-wise classification task, where logging curves are treated as one-dimensional depth-dependent signals. Although such methods have demonstrated promising performance, they usually rely on large-scale labelled datasets for training. Moreover, logging datasets commonly exhibit severe class imbalance due to complex geological environments and strong heterogeneity, which further degrades the performance and robustness of data-hungry deep learning models.
To address these challenges, we propose a novel lithology segmentation framework, in which we reformulate lithology classification as a semantic segmentation task, where different lithological units are characterized by continuous intervals separated by distinct boundaries along the depth dimension. Based on this formulation, we develop a lithology segmentation framework that leverages large-scale vision foundation models, enabling effective learning under data-scarce and class-imbalanced conditions. Our core motivation is to transfer the strong image representation and generalization capabilities learned by large pretrained models on massive image data to the geological logging domain.
Specifically, well logging curves are transformed into two-dimensional pseudo-images by a structured multi-scale channel combination along the depth dimension. The repetition factor k controls how many times each logging curve is duplicated in the pseudo-image, enabling Vision Transformer (ViT) with fix-sized patches to encode logging patterns at multiple effective scales. For each scale k, a composite representation X(K)∈ RH×WK is formed by repeating selected logging curves with scale-dependent repetition factors, where H is the number of depth samples. Accordingly, the width of the pseudo-image at scale k is defined as Wk = k·N, where N is the number of logging curves. The final input representation X is obtained by concatenating all scale-specific representations: X = Concat(X(1), X(2), X(2), ..., X(K)).
Building upon the pretrained Segment Anything Model (SAM), we retain the image encoder to extract high-level visual features, while a task-specific decoder is initialized and trained from scratch for lithology segmentation. The encoder weights are initially frozen and gradually unfrozen during training, and fine-tuned jointly with the decoder to adapt the feature space to the geological patterns of the specific domain. This staged training strategy stabilizes the optimization process, reduces overfitting with limited data, and effectively transfers knowledge from natural images to well logging images. Furthermore, by using a weighted loss function at the segmentation level to address class imbalance, it ensures that a minority of lithological classes contribute sufficiently to model updates.
Overall, the proposed framework demonstrates a new workflow for lithology interpretation by integrating foundation models with geological data analysis. It provides a data-efficient solution for lithology segmentation under realistic constraints of limited and imbalanced well logging datasets.
How to cite: Qian, N., Jagert, F., and Sester, M.: Lithology Segmentation from Well Logs for Geothermal Exploration using Vision Foundation Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7749, https://doi.org/10.5194/egusphere-egu26-7749, 2026.
Hot dry rock (HDR) represents a promising form of clean and renewable geothermal energy, with substantial global potential to support the transition to low-carbon energy systems. Among the most prospective regions for HDR development in China is the Gonghe Basin in Qinghai Province. However, the basin's complex subsurface geological conditions present significant challenges for the accurate assessment of HDR resources.
This study proposes a multi-scale integrated geophysical framework for HDR characterization, combining gravity, magnetic, magnetotelluric (MT), ambient noise tomography, and time-frequency electromagnetic methods. Multi-source geophysical datasets were systematically processed, calibrated with available borehole data, and interpreted through inversion modeling to construct a three-dimensional geological-geophysical model of the study area.
The results reveal the spatial distribution, burial depth, and thermal-structural properties of HDR reservoirs, identifying a high-potential zone with reservoir temperatures exceeding 200 °C. The integrated approach effectively addresses the limitations of individual geophysical methods, significantly enhancing the accuracy of HDR reservoir identification and parameter estimation. This study demonstrates the feasibility and effectiveness of integrated geophysical techniques in HDR exploration, offering a robust technical basis for future development in the Gonghe Basin and similar geothermal environments worldwide.
How to cite: lian, S., cheng, Z., wei, Q., and zhang, L.: Integrated Geophysical Characterization of Hot Dry Rock Resources in the Gonghe Basin, Qinghai, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7708, https://doi.org/10.5194/egusphere-egu26-7708, 2026.
Climate change drives urgent action in decarbonisation, and carbon capture and storage (CCS) has emerged as a crucial technology in mitigating greenhouse gas emissions. Large-scale subsurface CO2 injection carries the inherent risk of inducing fault reactivation and microseismic events, which could compromise the project. To optimise CCS projects while mitigating these geological hazards, passive acoustic emission (AE) monitoring offers a real-time method to detect initial fracture activity before failure.
In this study, triaxial compression experiments were conducted on reservoir-analogue sandstone sample plugs. Intact samples were axially loaded under an initial confining pressures (Pci) with continuous passive AE recording. A shear fracture was then induced in each sample, which was subsequently re-sheared under different confining pressure regimes (Pc) to mimic fault reactivation. Two porosity groups (~20% and 26%) were tested to evaluate deformation effects on AE response. Acoustic sensors at the sample ends captured the P-wave signals throughout each loading cycle, and the AE events were analysed in conjunction with the mechanical stress-strain data. From these mechanical data, failure envelopes were derived to assess the applicability of failure criteria. The results show that the Mohr–Coulomb criterion provides good agreement with all tests conducted and that fractured specimens may exhibit friction angles different from intact rock while retaining a non-zero cohesion, which should not be neglected when modelling fractured reservoirs for CCS.
The acoustic emission results reveal clear precursor patterns to fracture slip. For intact samples, axial loading triggered intense AE activity from the outset, reflecting micro-cracking and particle rearrangement. In contrast, samples with pre-existing fractures showed an initially low rate of emissions, increasing significantly just before the peak stress. Notably, higher-porosity samples generated roughly an order of magnitude more emissions than lower-porosity samples during both the initial fracturing and the reactivation phases, and consequently a much higher cumulative acoustic energy release.
Crucially, the cumulative AE record revealed a distinct acoustic precursor to failure. During re-shearing, the cumulative event count initially increased steadily, then underwent a sudden acceleration (an identifiable inflection point) shortly before the peak stress. This surge in event rate consistently occurred when the sample was still below its peak strength, signalling imminent failure. Such a signal could serve as an early warning. In a field injection scenario, detection of this acoustic inflection would allow operators to adjust injection rates or pressures before fault reactivation. Incorporating passive AE monitoring in this way could enhance CCS safety by optimising operations and preventing induced seismicity.
How to cite: Mesquita, R., Forbes Inskip, N., Naderloo, M., Barnhoorn, A., Doster, F., and Busch, A.: Early Warning of Fault Reactivation through Passive Acoustic Emission in Samples Analogous to Carbon Storage Reservoir, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1788, https://doi.org/10.5194/egusphere-egu26-1788, 2026.
The anion-accessible porosity fraction (fa) is an important parameter controlling solute transport in claystones. A safe disposal of nuclear waste in such rocks relies on a comprehensive understanding of transport in clays. In stratigraphic sequences, established Cl or Br profiles provide insights into the paleo-hydrogeological evolution. Anion concentrations in the accessible porewater can be calculated from measured bulk porewater concentrations and the anion-accessible porosity fraction fa. Since experimental data for fa are scarce, and data extrapolation within a heterogeneous stratigraphy is challenging due to the fa dependency on multiple parameters (e.g., pore/grain shapes and size distributions), detailed understanding of texture and its influence on macroscopic transport parameters is paramount.
In this study, imaging techniques (µCT and SEM) and other methods (e.g., N2 adsorption) were combined to characterise texture and the pore network of rock samples from Opalinus Clay and confining units. The techniques unravel pore characteristics at different scales: N2 adsorption from nanometers to a micrometer, SEM larger than 50 nm, and µCT larger than a few µm. Samples with different mineralogical compositions, lithologies, and experimentally determined fa for Cl (fCl) were analysed.
Two sand/siltstone samples with different fCl but similar clay content show identical ratios of grains to porous clay regions, but different pore sizes in high-resolution SEM images. This can qualitatively explain the different fCl for these samples. However, SEM cannot resolve small pores (<50nm), and a structural model is additionally required to derive quantitative results.
The gained textural insights make clear that fCl does not necessarily correlate with the clay fraction. Moreover, extended correlations of fCl with quantified textural information allow a better prediction of fCl for formations where this parameter was not measured. The outcome of this study encourages further investigations for verifications such as transmission electron microscopy (TEM) imagery to explore the nanometric pore space within and around clay minerals.
[1] C. Zwahlen, T. Gimmi, A. Jenni, M. Kiczka, M. Mazurek, L. R. Van Loon, et al., "Chloride accessible porosity fractions across the Jurassic sedimentary rocks of northern Switzerland," Appl. Geochem., vol. 162, p. 105841, 2024. DOI: 10.1016/j.apgeochem.2023.105841
How to cite: Zwahlen, C., Gimmi, T., Jenni, A., and Wüst, R.: Influence of texture on anion-accessible porosity fraction explored by µCT, SEM & N2 adsorption data , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6683, https://doi.org/10.5194/egusphere-egu26-6683, 2026.
Clays and clay rocks are relevant materials in many natural or engineered systems. Particles and pores in these materials are very small, which results in very low permeabilities. Accordingly, clays or clay rocks are considered as sealing materials or as host rocks for the safe disposal of hazardous waste in the underground.
The architecture of the pore space, i.e., the pore size distribution and the pore connectivity, are fundamental characteristics that define macroscopic properties of these materials, such as water retention function, hydraulic conductivity, diffusion coefficients, or the mechanical behavior. Unfortunately, the resolution of imaging techniques is often insufficient for a direct visualization of all pores in clays, and mostly indirect methods have to be used. Moreover, porewater close to charged clay surfaces may be partly bound, and this can also affect hydraulic conductivities.
We applied a range of different methods (Hg injection, N2 and H2O ad-/desorption, simultaneous thermal analysis coupled with evolved gas analysis STA-EGA) to characterize the pore space architecture and physical properties of porewater of a set of twelve very different rocks. We addressed the following questions: (1) Is porewater close to solid surfaces more strongly bound compared to porewater far from surfaces? (2) Are physical porewater properties related to basic properties of the clay rocks, such as clay-mineral content, cation exchange capacity, or the pore solution composition?
When comparing pore size distributions derived from the above methods and from NMR cryoporometry (Fleury et al., 2022), we see that mostly similar size ranges are obtained, but specific peaks should not be overinterpreted. Only NMR cryoporometry allows measurements at the original saturation state (except for high salinity solutions), which minimizes potential artefacts from drying. During STA, mainly water was released up to ~200°C (heating rate 10°C/min) in all samples. Vaporization enthalpy distributions derived from the STA data – indicators of water binding states – are unimodal in many cases, meaning that no clearly distinct water populations exist. However, the width of the distributions varied considerably among the samples. Comparably narrow distributions with a main peak in the region of bulk water vaporization enthalpies were seen for samples with relatively large pores, and wider or very wide distributions for samples with small pores, complex pore networks, higher surface charge concentration per volume of pore water, or high salinity pore solutions. The latter demonstrates that the derived vaporization enthalpies do not only reflect surface interactions, but also interactions with solutes. Finally, the partly large differences in the energetic state of the porewater should be considered as a relevant pore-scale feature when trying to derive macroscopic hydraulic parameters.
Fleury, M., T. Gimmi, M. Mazurek (2022). Porewater content, pore structure and water mobility in clays and shales from NMR methods. Clays Clay Miner. 70, 417–437, https://doi.org/10.1007/s42860-022-00195-4
How to cite: Gimmi, T., Mazurek, M., and Emmerich, K.: Pore space architecture and water binding state in clay-rich rocks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19147, https://doi.org/10.5194/egusphere-egu26-19147, 2026.
Various types of porous media (both unconsolidated and consolidated geological bodies and engineering materials, etc.) and fluids (water, gas, oil, supercritical carbon dioxide, etc.) are closely intertwined with multiple fields such as the environment, geology, and geotechnical engineering, involving soil contamination and groundwater remediation, high-level nuclear waste disposal, carbon dioxide storage, shale oil and gas extraction, hydrogen energy storage, and geothermal utilization. Nano-petrophysical studies focus on rock properties, fluid properties, and the interaction between rocks and fluids, especially for low-permeability geological and engineering media with a large number of nano-scale pores, as their microscopic pore structure (pore size distribution, pore shape and connectivity) controls the macroscopic fluid-rock interaction and the efficient development or preservation of various energy fluids. Such a subsurface system involves a wide range of nm-μm scale pore sizes, various pore connectivity and wettability, in addition to the coupled thermal-hydraulic-mechanical-chemical (THMC) processes of deep earth environments. This work showcases the development and application of an integrated and complementary suite of nano-petrophysical characterization approaches, including pycnometry (liquid and gas), porosimetry (mercury intrusion, gas physisorption), imaging (Wood’s metal impregnation followed with field emission-scanning electron microscopy), scattering (ultra- and small-angle neutron and X-ray), and the utility of both hydrophilic and hydrophobic fluids as well as fluid invasion tests (imbibition, diffusion, vacuum saturation) followed by laser ablation-inductively coupled plasma-mass spectrometry imaging of different nm-sized tracers in porous materials. These methodologies have been extended into coupled THMC processes under reservoir-relevant setting, such as the small-angle scattering (SAS) method developed and utilized for the direct observation of rock deformation behavior at a spatial resolution of 1 nm with stresses up to 164 MPa using a self-developed high-pressure cell for mechanistic studies of fluid-solid coupling.
Acknowledgement: This work was supported by the Basic Science Center Program of the National Natural Science Foundation of China (NSFC) (Type A; No. 42302145) and the International Cooperation Project of PetroChina (2023DQ0422).
How to cite: Hu, Q., Hao, F., Xiao, Y., Liu, K., Zhang, T., Ke, Y., Cheng, H., Li, X., Wang, Q., Zhao, C., and Yang, S.: Microscopic pore structure and macroscopic fluid flow-chemical transport with the coupled thermal-hydraulic-mechanical-chemical processes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15580, https://doi.org/10.5194/egusphere-egu26-15580, 2026.
In the production phase of geothermal resource development and utilization, the SCADA system for geothermal well scale inhibitor injection plays a critical role in scale prevention. A cyberattack on this SCADA system can result in production data anomalies and equipment damage, triggering a cascading failure: the inhibitor injection may be interrupted, leading to wellbore scaling and a reduction in thermal energy supply. As this impact propagates to the geothermal plant, it can reduce power generation, triggering voltage and frequency fluctuations in the grid that ultimately threaten power supply security. Currently, deep learning-based network security protection technologies have become an effective means to address these threats. However, the lack of high-quality, scenario-specific datasets restricts the effectiveness of this approach. Therefore, this paper aims to develop a method for generating a network intrusion detection dataset for the SCADA system of geothermal well scale inhibitor injection. Specifically, first, a geothermal well SCADA network testbed that closely aligns with the real process was constructed. On this testbed, multi-dimensional network attack experiments—covering scanning, denial-of-service (DoS), ARP spoofing, and man-in-the-middle (MitM) attacks—were systematically conducted to simulate threat scenarios with different origins, stealth levels, and scopes. Subsequently, network traffic data under both normal and attacked conditions were collected. The raw traffic was parsed and subjected to feature engineering, and data labeling was completed based on the alignment between attack logs and timestamps. Ultimately, we generated a dataset that contains over 25 million training samples and 2.5 million test samples. Based on this dataset, we conducted benchmark training and evaluation on four mainstream deep learning models: DNN, CNN, LSTM, and Transformer. The experimental results demonstrate that the generated dataset exhibits good learnability and can effectively support the training of different deep learning models. This study not only addresses the scarcity of specialized datasets in this field but also provides a reliable experimental foundation and evaluation benchmark for subsequent cybersecurity research in geothermal energy systems.
How to cite: Sun, H., Zhang, Y., Wan, H., Wei, K., Shui, Q., and Wang, H.: Research on the Generation Method of an Intrusion Detection Dataset for SCADA Systems in Geothermal Well Scale Inhibitor Injection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2164, https://doi.org/10.5194/egusphere-egu26-2164, 2026.
Geochemically, the oxygen isotope values range from −7.3‰ to −10.7‰, and hydrogen isotope values range from −72.6‰ to −57.2‰. Most data plot along the meteoric water line, indicating a dominant meteoric origin, while a small number of samples deviate slightly from this line, possibly reflecting fluid fractionation associated with boiling. An integrated three-dimensional geothermal geological model was constructed using: (1) surface DEM data, (2) regional geological maps and cross-sections, (3) borehole core descriptions and lithologic logs, (4) 3-D MT data, and (5) well temperature measurements. The Lishan Fault, located on the western margin of the Lushan geothermal area, is a highly active fault that has created a favorable fracture network serving as conduits for meteoric water infiltration and as conditions for geothermal reservoir development. Combined with previously acquired MT profiles across central Taiwan, the data reveal a low-resistivity zone extending upward from depth in the southwestern region along the Lishan Fault and spreading eastward into the Lushan geothermal area. This indicates that the primary heat/fluid source of the Lushan geothermal system is derived from deep circulation originating in the southwestern subsurface of the region.
Veins in the Lushan geothermal area are dominated by quartz veins, with minor occurrences of calcite veins. Based on field occurrences, the veins can be classified into three successive stages: (1) quartz veins parallel to slaty cleavage with homogenization temperatures between 220 and 300 °C, and salinities ranging from 5.7 to 9.1 wt.%, (2) quartz veins cutting across slaty cleavage with temperatures mainly between 220 and 290 °C, with salinities of 4.0–8.0 wt.%, and (3) euhedral to subhedral crystals infilling fractures and pores, yielding homogenization temperatures mostly between 220 and 300 °C, with salinities of 3.1–9.7 wt.%, whereas calcite-hosted fluid inclusions show lower homogenization temperatures of 150–210 °C and salinities of 1.0–5.7 wt.%. Comparison of fluid inclusion temperatures indicates that similarly high homogenization temperatures were attained during all three stages. No clear correlation is observed between temperature and salinity, and the salinity distributions are comparable among different stages. These features suggest the presence of a stable brine source constrained by synclinal structures in the region. The fluids are inferred to originate from a persistent deep heat source beneath Chunyang, where they were heated at different depths before ascending and precipitating mineral veins during successive tectonic episodes.
How to cite: Song, S.-R., Wang, Y.-C., Song, T.-J., and Lu, Y.-C.: Source and Evolution of Thermal Fluids in the Lushan Geothermal Field, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2780, https://doi.org/10.5194/egusphere-egu26-2780, 2026.
Running detailed, physics-based numerical simulations of subsurface transport is often computationally expensive. This becomes a challenge when calibrating models against observed data using methods that require a large number of model runs, such as Bayesian inference. To address this challenge, surrogate models are frequently used to approximate simulation outputs. Surrogates are trained using input-output pairs generated by the physics-based model. Traditional approaches typically rely on space-filling designs that uniformly cover the entire parameter space. However, for high-dimensional problems, this becomes impractical and tends to waste computational resources on parameter regions that are either physically irrelevant or contradict available measurement data.
To overcome these limitations, we utilize a Bayesian Active Learning (BAL) framework that iteratively selects training points most informative for Bayesian inference given available measurements. We employ Gaussian Processes and Bayesian-Polynomial Chaos Expansions as surrogates, which provide probability distributions for their predictions. Our approach takes advantage of these predictive distributions to evaluate candidate training points using information-theoretic criteria. To account for measurement uncertainty and prevent the algorithm from over-sampling local likelihood maxima, we investigate different strategies for representing observations within the selection process. These criteria are integrated into a multi-objective scoring function that balances global exploration (reducing surrogate uncertainty) with targeted exploitation (refining high-likelihood regions). Additionally, we demonstrate how observations from early time steps can iteratively guide the selection of training points to improve predictive accuracy for later, critical periods of the transport process.
We test this method on analytical benchmarks and on subsurface transport models. The framework is evaluated in terms of convergence speed and posterior accuracy relative to existing active learning strategies and reference solutions derived from the full physics-based model. Overall, the proposed goal-oriented strategy aims to reduce the number of expensive model evaluations required for surrogate training, improving the efficiency of subsurface characterization, model calibration and predictive modeling.
How to cite: Morales Oreamuno, M. F., Brünnette, T., Scheurer, S., Oladyshkin, S., and Nowak, W.: Information-Theoretic Bayesian Active Learning for Surrogate Training and Inverse Modeling in Subsurface Transport Applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4056, https://doi.org/10.5194/egusphere-egu26-4056, 2026.
Underground hydrogen storage (UHS) is a cornerstone technology for net-zero energy systems, offering terawatt-hour capacity to buffer renewable intermittency. Although many experiments have been reported on hydrogen flow in porous rocks, robust evidence for long-duration reactions and impact on transport under combined high temperature and high pressure remains limited, leaving a critical uncertainty around reservoir stability during seasonal storage.
Here we provide firm, multi-scale pre/post experimental constraints on two major onshore UK candidate aquifers—the Triassic Sherwood Sandstone Group and the Cretaceous Lower Greensand Group—after ~3 months exposure to H₂ at simulated in-situ conditions deep underground, 50 °C and 150 bar. We integrate X-ray computed tomography (3D pore–grain architecture and bulk phase fractions), optical petrography (fabric/facies), SEM imaging (micro-textures and fines), and XRD (mineralogy) to resolve hydrogen impacts across scales. We also performed dynamic synchrotron images of hydrogen flows in the porous rocks to investigate the reaction impact on the transport. We performed systematically investigations on the pore networks, grain framework, or mineralogy, porosity and permeability. The results show the pore network changes varied by <5%, consistent with measurement uncertainty. Only a single localised fines-migration feature (likely pyrite grain displacement) was detected, without associated dissolution/precipitation signatures. Quartz-dominated frameworks (>~65 wt%) appear inert under these conditions, while facies-scale heterogeneity governs pore connectivity and is expected to dominate injectivity and withdrawal behaviour. These results reduce a key uncertainty for UHS in silicate-rich sandstones, support prioritising connected macro-porous facies in site screening and well placement, and provide a transferable workflow for rapid hydrogen–rock interaction assessment and monitoring. Future work should extend to potentially more reactive lithologies, cyclic operation, longer exposure, and bio-active systems, in order to complete risk evaluation for large-scale seasonal storage.
How to cite: Ma, L., Braid, H., Taylor, K., Hough, E., and Rochelle, C.: From Pore to Core: Multi-Scale Evidence of Underground Hydrogen Storage Stability After Three Months of Hydrogen Exposure Under Reservoir Conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4411, https://doi.org/10.5194/egusphere-egu26-4411, 2026.
Understanding the nucleation, growth, and persistence of CO2 gas phases in water-saturated porous media is critical for predicting fluid transport, trapping efficiency, and integrity in geological CO2 storage systems. Gas exsolution under depressurisation remains poorly constrained at the nano- to micro-scale, where capillarity, confinement, and surface chemistry strongly influence phase behaviour. In this study, we investigate CO2 exsolution from water saturating a clay-rich sandstone using small-angle neutron scattering (SANS) under realistic reservoir conditions, providing direct, in situ insights into gas phase evolution within the pore space.
SANS experiments were conducted at the EQ-SANS instrument at Oak Ridge National Laboratory using a pressure cell allowing for exsolution testing at 50 °C under cyclic depressurisation from 12 MPa to 0.7 MPa. The pore fluid consisted of a contrast-matched H2O–D2O mixture (68:32 vol.%), yielding a stable scattering length density of 4.17 × 1010 cm-2, similar to that of the matrix. The H2O:D2O mixture was saturated with CO2 at 12 MPa and room temperature (~22 °C) prior to controlled pressure reduction. Under these conditions, the scattering signal arises from exsolved CO2 nanobubbles. SANS profiles were obtained continuously during pressure decrease.
The scattering data reveal the emergence and evolution of nanoscale heterogeneities consistent with CO2 gas clusters and nanobubbles forming within pores between 5 and 200 nm. Although phase diagrams predict CO2 exsolution at about 8 MPa and 50 °C, this is only observed at ~2.4 MPa. Changes in scattering intensity and slope indicate pressure-dependent growth and coalescence processes, influenced by pore confinement and clay mineral surfaces. Notably, a progressive loss of scattering signatures associated with pores smaller than ~15 nm during pressure reduction suggests the preferential disappearance of CO2 nanobubbles in the smallest pores. This is potentially driven by Ostwald ripening, whereby gas diffuses from high-curvature, unstable nanobubbles toward larger, more stable gas clusters. Repeated pressure cycling highlights the partial reversibility of exsolution and the persistence of gas features, suggesting potential hysteresis effects relevant for cyclic injection and pressure management strategies.
These findings demonstrate the capability of SANS to resolve nanoscale CO2 exsolution processes in complex geomaterials and provide critical constraints for pore-scale and continuum models of multiphase flow and transport. The results have direct implications for assessing CO2 mobility, trapping mechanisms, and leakage risk in clay-rich storage formations and caprocks under dynamic pressure conditions.
How to cite: Busch, A., Rezaeyan, A., Rother, G., Jangda, Z., Menke, H. P., and Singh, K.: CO2 Exsolution and Nanobubble Evolution in Sandstone under Cyclic Depressurisation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6239, https://doi.org/10.5194/egusphere-egu26-6239, 2026.
The degradation of rock mass strength is macroscopically manifested as a reduction in cohesion and an increase in the internal friction angle. Microscopically, it manifests as the propagation of internal fractures, which is also the fundamental cause of rock mass damage and deterioration. The complex mesoscopic fracture structure within the rock mass directly influences its macroscopic mechanical properties and failure characteristics. To more accurately understand the mechanical behavior of rock masses under unloading conditions, it is essential to investigate the internal mesoscopic fracture structure of the rock and its impact on the overall mechanical properties.
To study the crack propagation and meso-damage evolution of saturated sandstone under unloading (unloading confining pressure), triaxial unloading confining pressure tests were designed and conducted on sandstone samples under different initial axial pressures (70%, 80%, and 90% of the triaxial compressive strength, TCS). The results indicate that samples with higher initial axial pressure exhibit larger axial strain and smaller radial strain at unloading failure. As the unloading confining pressure ratio increases, the elastic modulus gradually decreases, while Poisson's ratio and strain gradually increase.
Using 1H Nuclear Magnetic Resonance (NMR) technology, the variations in rock porosity and T2 spectrum curves were analyzed. The T2 spectral peaks show that pore size increases with the unloading confining pressure ratio, and a higher initial axial pressure leads to more significant pore size growth. Porosity increases exponentially with the unloading confining pressure ratio. Within this trend, the number of micropores continuously increases, whereas the numbers of mesopores and macropores first decrease and then increase. The initial axial pressure promotes the development and expansion of pores.
The fractal characteristics of the T2 spectrum were analyzed, and the relationship between the degree of damage and the unloading confining pressure ratio was established. The variation trends of rock pore characteristics, energy, and damage degree are generally consistent. Finally, based on damage mechanics theory, a damage constitutive model for rock under loading and unloading conditions was developed. The overall correspondence between the theoretical model predictions and the experimental curves is satisfactory.
How to cite: Zhang, C., Wang, J., Chen, W., Fu, J., and Song, W.: Experimental study on pore variation and meso-damage of saturated sandstone under unloading condition, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6919, https://doi.org/10.5194/egusphere-egu26-6919, 2026.
Deep carbonate gas reservoirs represent a crucial frontier in natural gas exploration. However, their strong heterogeneity and complex pore structures often lead to technical challenges and low recovery rates. The Dengying Formation in the Penglai gas area, Sichuan Basin, characterized by typical vuggy, fracture-vuggy, and porous reservoir types, serves as an ideal focus for addressing these issues. Nevertheless, conventional core flooding experiments lack in-situ visualization and real-time monitoring capabilities, making it difficult to characterize dynamic fluid migration at the microscopic scale. Therefore, establishing a new experimental methodology is urgently needed.Moreover,This experiment employed CO2 as the displacement gas. The injection of CO2 into deep carbonate formations enables the underground storage of greenhouse gases, realizing carbon sequestration with substantial environmental benefits.
In this study, typical carbonate samples from the Dengying Formation were selected to conduct high-temperature and high-pressure (HTHP) physical simulation experiments of SCCO2 displacing methane (CH4) using X-ray Computed Tomography (X-CT). A complete experimental workflow covering "formation water saturation, gas charging, and SCCO2 displacement" was established, along with a quantitative parameter system. Through real-time online monitoring, fluid migration patterns and displacement characteristics were quantitatively analyzed based on CT images and CT number variations.
The results indicate that: (1) Fracture-vuggy reservoirs exhibit the best displacement performance under high pressure, with the sweep volume of SCCO2 expanding progressively over time. (2) In fracture-dominated reservoirs, SCCO2 tends to migrate along preferential "fracture-vug" pathways under high pressure, leading to gas channeling (fingering) and low sweep efficiency; optimizing the pressure differential (reducing displacement rate) can effectively mitigate channeling and improve matrix mobilization. (3) Vuggy reservoirs have a high mobilization threshold, requiring a higher pressure gradient and longer displacement duration, with the sweep zone expanding gradually. (4) Porous (tight-matrix) reservoirs show the poorest performance; due to narrow throats and poor connectivity, high seepage resistance prevents significant saturation changes or displacement fronts from being observed in CT images.
This study reveals the microscopic mechanisms of SCCO2 displacing gas under different carbonate pore structures and clarifies the control of heterogeneity on displacement efficiency, providing theoretical support for Enhanced Gas Recovery (EGR) and CO2 sequestration in deep carbonate reservoirs.
Keywords: Deep carbonate reservoir; X-CT scanning; SCCO2-EGR; Physical simulation; Dengying Formation
How to cite: Zhang, Z., Liu, J., and Liu, K.: Microscopic Mechanism of SCCO2 Displacing CH4 in Deep Carbonate Gas Reservoirs Based on X-CT Scanning: A Case Study of the Dengying Formation, Penglai Gas Area, Sichuan Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8605, https://doi.org/10.5194/egusphere-egu26-8605, 2026.
Parameter calibration remains a critical bottleneck in coupled thermo–hydro–mechanical–chemical simulations, particularly when parameters are strongly coupled and non-unique solutions exist. In OpenGeoSys (OGS), calibration is frequently performed by manual trial-and-error, resulting in workflows that are subjective, difficult to reproduce, and unsuitable for systematic comparison of calibration strategies. These limitations become especially pronounced in multiphysics settings, where equifinality can mask parameter sensitivity and bias interpretation.
This study presents a non-intrusive, reusable Python framework for automated parameter calibration in OGS that treats the simulator as a black-box forward model. The framework controls the complete calibration workflow externally, including parameter sampling within defined bounds, automated execution of OGS simulations, extraction of user-defined parameters from output files, and quantitative misfit evaluation using different metrics. A total of twelve optimization algorithms are integrated, spanning local deterministic methods, surrogate optimization, population and swarm based approaches, and hybrid strategies. All algorithms are accessed through a unified configuration interface, enabling direct and fair benchmarking under the same evaluation metrics.
The framework is evaluated using an axisymmetric hydro-mechanical borehole benchmark with prescribed pressure and stress histories. Intrinsic permeability and Young’s modulus are jointly calibrated against a reference mass-flow time series, with each optimization method limited to approximately 100 forward simulations. The results demonstrate that calibration performance is governed primarily by misfit reduction efficiency per simulation rather than algorithmic overhead. Population-based methods robustly identify favorable regions of the parameter space, local search methods exhibit rapid convergence near optimal solutions, and hybrid strategies consistently combine both strengths. The proposed framework provides a reproducible and objective basis for parameter calibration in OpenGeoSys, enabling the development of more reliable models for coupled multiphysics applications.
How to cite: Mahmoudi Kouhi, R., Taherdangkoo, R., Nagel, T., and Butscher, C.: A Python Multi-Algorithm Optimization Framework for Automated Parameter Calibration in OpenGeoSys, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11018, https://doi.org/10.5194/egusphere-egu26-11018, 2026.
Shallow geothermal wells for heating individual houses have been utilised successfully in Finland for decades. Recently, deep geothermal wells (down to six kilometres) and medium deep wells (500–3 000 metres) have been piloted for district heating, regional-scale applications in urban areas, and industrial building heating, with varying degrees of success.
Bedrock in Finland consists mostly of Precambrian crystalline rocks. Ancient bedrock is cold and fractured. Several geothermal projects have been delayed, shortened or even cancelled due to challenges in drilling or insufficient heat production. In Geoenergialoikka (Geoenergy Leap) project, the Geological Survey of Finland (GTK) has been developing a workflow to integrate geodata for the site selection of medium deep geothermal wells and consequently for estimating heat production, aiming to improve the success rate of geothermal projects.
The workflow has been developed while planning and implementing three medium deep geothermal wells (600–800 m) in Kotka, Oulu and Kokkola. Existing geodata has been utilised to determine the locations of the proposed geothermal wells and to assess drilling risks. The datasets include geodata available from GTK’s Hakku service, e.g. geological and aerogeophysical maps, Lidar and lineament data, as well as site specific geophysical survey data. The cost-effectiveness of different data analyses and survey methods has been evaluated, and best practices for utilizing geodata in medium deep geothermal projects will be proposed.
Geoenergialoikka is co-funded by the European Union’s Just Transition Fund (JTF), the councils of Central Ostrobothnia, North Ostrobothnia, and Kymenlaakso, and the project partners: Geological Survey of Finland GTK (the coordinator), Centria University of Applied Sciences, Oulu University of Applied Sciences OAMK, University of Oulu, and South-Eastern Finland University of Applied Sciences XAMK. The project aims to speed up the comprehensive use of geothermal energy, strengthening national energy self-sufficiency and supply security, and impacting regional employment positively.
How to cite: Luhta, T., Martinkauppi, A., Karjalainen, A., and Laakso, V.: Utilising geodata for enhancing success rate of geothermal projects in Finland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11701, https://doi.org/10.5194/egusphere-egu26-11701, 2026.
Carbonate terrains in central Saudi Arabia are prone to subsurface hazards due to karstification, fracturing, and differential weathering, posing significant risks for large infrastructure developments. This research presents an integrated geophysical and geotechnical investigation carried out for the proposed construction site at Riyadh, located along the Wadi Hanifah escarpment. The site is underlain by highly to moderately weathered Jurassic limestone of the Shaqra Group, characterized by vugs, fractures, and solution-filled discontinuities.
Multichannel Analysis of Surface Waves (MASW) was employed to map subsurface stiffness variations and identify potential cavities and weak zones. A total of 1400 linear meters of MASW profiles were acquired using a 24-channel system with 2.5 m geophone spacing, achieving an investigation depth of up to approximately 25 m. Shear-wave velocity (Vs) sections were generated through dispersion analysis and inversion of surface-wave data. The interpreted Vs values range from about 200 m/s to 3500 m/s, where higher velocities (>1500 m/s) represent competent limestone, while lower velocities (<1000–1500 m/s) indicate fractured, weathered, or solution-affected zones.
MASW results delineated several localized low-Vs anomalies corresponding to solution-filled vugs and cavities at depths ranging from approximately 1 m to 13.5 m. These geophysical findings were correlated with borehole data from fifteen geotechnical boreholes, including rock coring, RQD measurements, pressuremeter testing, and laboratory strength testing. Borehole logs confirm the presence of highly fractured limestone with variable RQD (0-100%) and unconfined compressive strength values between about 13 and 65 MPa. Zones identified as weak in MASW sections coincide with intervals of low RQD, poor core recovery, and solution-filled fractures observed in the boreholes.
The integrated interpretation demonstrates that MASW is an effective tool for rapid detection and lateral mapping of karst-related weak zones in limestone terrains when calibrated with geotechnical data. The results provided critical input for foundation design, ground improvement planning, and risk mitigation at the site. This study highlights the value of combining surface-wave geophysics with conventional geotechnical investigations for sustainable and safe development in karst-prone regions.
How to cite: Jadoon, M. A. and Jadoon, K. Z.: Integrated Geophysical and Geotechnical Investigation for Detection of Karstic Weak Zones in Limestone Terrain, Riyadh, Saudi Arabia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15860, https://doi.org/10.5194/egusphere-egu26-15860, 2026.
Aquifer Thermal Energy Storage (ATES) offers sustainable, low carbon heating and cooling to the built environment. Optimising the design and operation of ATES installations requires numerical simulation of groundwater flow and heat transport in heterogeneous aquifers. These simulations are typically computationally expensive: high spatial resolution is required to accurately resolve pressure, flow and temperature fields; moreover, high temporal resolution may be necessary to control numerical diffusion and/or resolve frequent changes in injection flowrate and temperature. Simulations of (1) systems that utilize multiple well doublets, or (2) capture interactions between neighbouring systems, are particularly challenging. Multiple simulations may be required to quantify the impact of uncertain aquifer heterogeneity. Yet the time available for aquifer modelling in many commercial projects is very limited. Rapid but accurate approaches to simulate subsurface flow and heat transport in ATES and other shallow geothermal deployments are urgently required.
Machine Learning (ML) offers a rapid alternative to conventional numerical simulation of complex subsurface flow and transport processes. Here we introduce the use of a transformer-based ML approach, on a purely data-driven basis, to significantly increase simulation efficiency whilst retaining its accuracy. The ML proxy is trained using ATES simulation outputs from the open-source Imperial College Finite Element Reservoir Simulator (IC-FERST), that uses dynamic mesh optimization to provide high solution accuracy at lower computational cost. The practical consequence here is that the mesh changes across solution snapshots recorded at successive time steps used for training. Conventional Convolutional Neural Network (CNN)-based models require a fixed mesh. Here, to provide a fast proxy, we implement atransformer-based model working on adaptive unstructured mesh, enabling a stronger capability in capturing long range changes in predictions. The model can take in the initial state of the reservoir in arbitrary input mesh, perform one-step prediction in non-physical latent space and recover the latent representation of the prediction back to physical space on any given query mesh, allowing the integration of adaptive mesh refinement adjusted to fit the predicted solution on unstructured graphs.
Our results suggest a promising approach to rapid simulation of ATES, in which simulation time can be reduced significantly with a speed-up factor of over 6600 times.
How to cite: Fung, H., Ju, I., Jacquemyn, C., Bahlali, M., Jackson, M., and Wen, G.: Rapid simulation of Aquifer Thermal Energy Storage using transformer-based Machine Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17391, https://doi.org/10.5194/egusphere-egu26-17391, 2026.
Heat-flow data are a critical input for geothermal exploration, lithospheric studies, and assessments of the global heat budget. Despite decades of measurements, their reuse has been hampered by heterogeneous or incomplete metadata, inconsistent quality assessment and documentation, and limited interoperability between regional and global compilations. To address these limitations, we present the new European heat-flow compilation as part of the World Heat Flow Database, now served through the www.heatflow.world platform as its new digital home. The European dataset comprises more than 14,000 heat-flow determinations from approximately 8,000 locations, including complementary data (e.g., underlying rock properties, measured temperature gradients, site-specific effects), and covering measurements acquired between 1939 and 2025. The dataset strictly follows a unified metadata schema and quality evaluation framework developed by the International Heat Flow Commission. This framework evaluates heat-flow determinations along three independent dimensions: methodological robustness, numerical uncertainty, and environmental or site-specific perturbations. These dimensions are combined into a transparent, reproducible quality score that supports objective comparison, automated filtering, and informed reusable data structure. Our analysis demonstrates that high-quality heat-flow data are available across most European regions, although the spatial density of data remains uneven. Importantly, data quality shows no systematic dependence on the year of measurement, underlining the long-term value of well-documented legacy data when embedded in a modern, quality-controlled framework. By integrating the European compilation into the World Heat Flow Database and publishing it via heatflow.world, regional datasets become interoperable components of a continuously expanding, standardised global resource. The heatflow.world platform is designed to follow the FAIR data principles, providing findable, accessible, interoperable, and reusable heat-flow data, grids, and maps for both academic and applied users. The online interface of the World Heat Flow Portal supports transparent data citation, community-driven updates, and long-term sustainability, thereby establishing a robust foundation for future geothermal exploration and global thermal studies.
How to cite: Norden, B., Elbarbary, S., Balkan-Pazvantoğlu, E., Petrunin, A., Karagiorgas, M., Neumann, F., Bernhard, R., Kopf, A., Elger, K., Jennings, S., Ott, N., Mäs, S., and Fuchs, S.: heatflow.world: A FAIR, Quality-Controlled Global Platform for Heat-Flow Data and Geothermal Applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17559, https://doi.org/10.5194/egusphere-egu26-17559, 2026.
Hsinchu is one of the most densely concentrated high-tech industrial cities in Taiwan and hosts the headquarters of Taiwan Semiconductor Manufacturing Company (TSMC). With the rapid development of artificial intelligence technologies, energy demand has increased sharply, highlighting the need for reliable and sustainable energy resources to mitigate the escalating power consumption. Shallow geothermal energy is a renewable resource that remains underutilized in Taiwan. Hsinchu City is located on Quaternary alluvial deposits characterized by a shallow groundwater table and relatively high groundwater flow velocities, which may provide favorable hydrogeological conditions for the utilization of shallow geothermal energy. This study aims to evaluate the shallow geothermal energy potential of Hsinchu City. The research begins with the construction of a three-dimensional geological model using an advanced geostatistical approach, namely the Bayesian Maximum Entropy (BME) method. The developed model provides spatially distributed information on subsurface thermal properties and groundwater dynamics. Subsequently, an analytical heat-transfer model, the Moving Infinite Line Source (MILS) model, is employed to back-calculate the maximum accessible heat extraction rate under two constraints: environmental impact limits and engineering design limits. The evaluation scenarios consider a 20-year operational period for vertical borehole heat exchanger systems under seasonal variations in groundwater depth and flow velocity. This preliminary assessment provides valuable insights into the feasibility and potential of shallow geothermal energy development and offers a scientific basis for future energy-saving strategies in the Hsinchu Science Park and surrounding industrial areas.
How to cite: Tseng, H., Hsu, S.-C., Huang, W.-C., Wang, C.-L., and Yu, H.-L.: Shallow Geothermal Potential Mapping Incorporating Groundwater Effects Based on a 3D Geological Model: Hsinchu, Taiwan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20127, https://doi.org/10.5194/egusphere-egu26-20127, 2026.
Reliable estimates of thermal conductivity and radiogenic heat production are essential for robust heat-flow calculations and geothermal assessments. In the high Arctic archipelago of Svalbard, geothermal energy is increasingly considered as an alternative to the present diesel-based energy supply. However, direct measurements of thermal properties are limited to shallow, fully cored research boreholes, while the deeper subsurface—where temperatures suitable for geothermal district heating (~80 °C) are reached at depths of ~2 km beneath the settlement of Longyearbyen—remains poorly constrained. In this study, we derive thermal properties for the Silurian (?) to Paleogene sedimentary succession of onshore Svalbard using wireline logs from eight petroleum exploration boreholes drilled to depths of up to 3.3 km. In addition, we include data from two fully-cored research boreholes. Lithology logs were digitized and used as the basis for thermal modeling. Two approaches were applied: (1) assigning generalized thermal properties based on lithology classes, and (2) calculating thermal properties directly from wireline logs, incorporating lithological information. The resulting thermal conductivity estimates range from 0.4 to 4.2 W m⁻¹ K⁻¹ and show strong lithological control. In the uppermost kilometer, calculated thermal conductivities were compared with laboratory measurements from two fully cored boreholes, revealing consistent lithology-dependent trends, although calculated values are generally slightly lower than measured ones. The derived thermal properties were subsequently used as input for 1D heat-flow modeling of the ten boreholes and a hypothetical deep geothermal borehole beneath Longyearbyen. Calculated heat-flow values range between 60 and 147 mW m⁻², with the highest values obtained for the Raddedalen borehole on Edgeøya. Our results demonstrate that wireline-log-derived thermal properties provide a valuable basis for improving heat-flow estimates and enable a more spatially resolved assessment of the thermal state and geothermal potential of Svalbard.
How to cite: Nuus, M., Senger, K., Fuchs, S., Smyrak-Sikora, A., and Kubutat, T.: Towards Sustainable Energy in Svalbard: Geothermal Heat-Flow Insights from Wireline Logs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21331, https://doi.org/10.5194/egusphere-egu26-21331, 2026.
Germany’s transition to renewable energy sources places increasing importance on the efficient use of shallow and medium-depth geothermal systems. The WärmeGut project supporting this energy transition, is funded by the Federal Ministry for Economic Affairs and Energy (Bundesministerium für Wirtschaft und Energie). A central element of this initiative is the development of a comprehensive database to systematically record and evaluate geothermal data obtained from Thermal Response Tests (TRTs) and temperature-depth profile measurements.
These measurements are essential for obtaining thermal properties for subsurface characterization, yet historically, the data have been fragmented, inconsistently stored, and often inaccessible to practitioners and researchers. The creation of a dedicated TRT Database addresses these gaps by enabling standardized data collection, quality control, and long-term storage, thereby supporting more reliable planning and simulation of geothermal systems.
To maximize its impact and usability, a key solution proposed is the integration of this TRT data module into the existing Geophysics Information System (https://www.fis-geophysik.de), a platform managed by the LIAG – Institute for Applied Geophysics. The Geophysics Information System currently provides structured access to a wide range of geophysical measurements and preliminary subsurface evaluations, such as underground temperature profiles. Incorporating TRT data will enhance the system’s value by linking thermal performance insights with broader geological and geophysical contexts.
Ultimately, this effort supports more informed decision-making in geothermal energy development across Germany, fosters research synergies, and contributes to the national goals of energy efficiency and climate resilience.
How to cite: Sardar Abadi, M., Rumohr, S., Jensen, H., Gramenz, J., Kuper, K.-M., and Agemar, T.: Thermal Response Test (TRT) Database Development and Integration for Shallow Geothermal Applications: The FIS-GP Example, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23124, https://doi.org/10.5194/egusphere-egu26-23124, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 4
EGU26-16204 | Posters virtual | VPS19
PINN-Based Digital Twins for Modeling Groundwater-Induced Subsurface Collapse under Low-Observability Hydro-Mechanical ConditionsTue, 05 May, 14:57–15:00 (CEST) vPoster spot 4
Groundwater-induced subsurface collapse presents a critical geotechnical hazard in karst terrains, which poses heavy risks to global public safety and infrastructure. Despite the substantial economic impact, predicting these failures remains challenging due to sparse subsurface monitoring and the difficulty of integrating indirect, multi-modal satellite data into traditional models. To address the challenge of low observability, we present a physics-informed neural network (PINN)-based digital twin for simulating coupled hydro-mechanical processes. The framework integrates NASA GPM (IMERG) precipitation data and Sentinel-1 InSAR surface deformation measurements to constrain subsurface dynamics. Implemented in the West-Central Florida Karst Belt, the model represents a three-dimensional domain of unconsolidated overburden overlying a weathered limestone aquifer. Subsurface dynamics are governed by transient Darcy flow and an effective stress relationship, while progressive material weakening is captured through a continuous damage variable, d, which evolves via stress redistribution and pore-pressure diffusion. Through minimizing the residuals of these governing equations, the PINN identifies the start of collapse, defined as the point where localized damage exceeds a critical threshold. Our results indicate that the digital twin produces physically consistent fields with 25–30% lower error in pore pressure and damage predictions compared to simulations that are uncoupled. Predicted collapse initiation times, Tc, remained within 18–23% of benchmark solutions, capturing time-accelerated failure during intense recharge events. Sensitivity analysis reveals that hydraulic conductivity, K, accounts for over 63% of damage variance, highlighting the model's physical interpretability. This framework provides a scalable approach for real-time hazard assessment in data-poor karst regions globally.
How to cite: Korada, S. and Liu, W.: PINN-Based Digital Twins for Modeling Groundwater-Induced Subsurface Collapse under Low-Observability Hydro-Mechanical Conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16204, https://doi.org/10.5194/egusphere-egu26-16204, 2026.
