Subsurface Geological Formations Research Articles

From Shallow to Deep: Enhancing Seismic Resolution with Weak Supervision

High-resolution seismic data are crucial for accurately identifying subsurface geological formations and reservoir properties. However, as widely observed in both prestack and poststack data, seismic wave attenuation often leads to a gradual decrease in resolution with depth. This degradation leads to resolution differences between data from shallow and deep layers, characterized by high-frequency energy loss in deeper target layers. The latter could complicate the extraction of crucial deep-layer structure information, leading to increased uncertainty in reservoir modeling and significant economic losses due to our inability to image deep targets with sufficient resolution. Traditional methods with simplified assumptions about this nonlinear attenuation hinder modeling geological complexity and variability. Deep learning methods excel at capturing complex, nonlinear relationships but often rely heavily on scarce and costly paired labels for supervised learning. As a result, deep learning without requiring paired labels for seismic resolution enhancement has become a key research focus. This study explores a method to enhance 3D seismic data resolution using weakly supervised learning, leveraging the inherent similarity of sedimentary structures across depths. Specifically, our approach utilizes a relatively shallow, higher-resolution data window to transfer and extrapolate learned high-frequency information to a deeper window containing attenuated responses from deeper layers. Thereby, we achieve enhanced resolution in the target region. A tailored 3D convolutional neural network with a bidirectional cycle structure, custom-designed loss functions, and data preprocessing techniques specifically addresses the challenge of resolution differences caused by seismic wave attenuation. The effectiveness of the proposed method is validated with synthetic and real 3D poststack migration data, demonstrating its robustness for regions near the target layer. Compared to conventional spectral whitening, our approach leverages intrinsic data characteristics more adaptively and robustly, making geological structures more discernible.#xD;

Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration

Geological carbon sequestration (GCS) involves injecting CO 2 into subsurface geological formations for permanent storage. Numerical simulations could guide decisions in GCS projects by predicting CO 2 migration pathways and the pressure distribution in storage formation. However, these simulations are often computationally expensive due to highly coupled physics and large spatial-temporal simulation domains. Surrogate modelling with data-driven machine learning has become a promising alternative to accelerate physics-based simulations. Among these, the Fourier neural operator (FNO) has been applied to three-dimensional synthetic subsurface models. Despite its good accuracy in simulating CO 2 plume migration, it requires large computational resources in training and also lacks generalizability. Here, to further improve performance, we have developed a nested Fourier-DeepONet by combining the expressiveness of the FNO with the modularity of a deep operator network (DeepONet). This new framework is twice as efficient as a nested FNO for training and has at least 80% lower GPU memory requirement due to its flexibility to treat temporal coordinates separately. These performance improvements are achieved without compromising prediction accuracy. In addition, the generalization and extrapolation ability of nested Fourier-DeepONet beyond the training range has been thoroughly evaluated. Nested Fourier-DeepONet outperformed the nested FNO for extrapolation in time with more than 50% reduced error. It also exhibited good extrapolation accuracy beyond the training range in terms of reservoir properties, number of wells, and injection rate.

Comprehensive Comparative Review of the Cement Experimental Testing Under CO2 Conditions

Global warming is presently one of the most pressing issues the planet faces, with the emission of greenhouse gasses being a primary concern. Among these gasses, CO2 is the most detrimental because, among all the greenhouse gasses resulting from anthropogenic sources, CO2 currently contributes the largest share to global warming. Therefore, to reduce the adverse effects of climate change, many countries have signed the Paris Agreement, according to which net zero emissions of CO2 will be achieved by 2050. In this respect, Carbon Capture and Sequestration (CCS) is a critical technology that will play a vital role in achieving the net zero goal. It allows CO2 from emission sources to be injected into suitable subsurface geological formations, aiming to confine CO2 underground for hundreds of years. Therefore, the confinement of CO2 is crucial, and the success of CCS projects depends on it. One of the main components on which the confinement of the CO2 relies is the integrity of the cement. As it acts as the barrier that restricts the movement of the sequestrated CO2 to the surface. However, in a CO2-rich environment, cement reacts with CO2, leading to the deterioration of its physical, chemical, transfer, morphological, and mechanical properties. This degradation can create flow paths that enable the leakage of sequestered CO2 to the surface, posing risks to humans, animals, and the environment. To address this issue, numerous studies have investigated the use of various additives in cement to reduce carbonation, thus enhancing the cement’s resistance to supercritical (sc) CO2 and maintaining its integrity. This paper provides a comprehensive review of current research on cement carbonation tests conducted by different authors. It includes detailed descriptions of the additives used, testing setups, curing conditions, methodologies employed, and experimental outcomes. This study will help to provide a better understanding of the carbonation process of the cement sample exposed to a CO2-rich environment, along with the pros and cons of the additives used in the cement. A significant challenge identified in this research is the lack of a standardized procedure for conducting carbonation tests, as each study reviewed employed a unique methodology, making direct comparisons difficult. Nonetheless, the paper provides an overview of the most commonly used temperatures, pressures, curing durations, and carbonation periods in the studies reviewed.

An Integrated Machine Learning Workflow to Estimate In Situ Stresses Based on Downhole Sonic Logs and Laboratory Triaxial Ultrasonic Velocity Data

AbstractThe optimum performance of various subsurface operations such as stimulation treatments, wellbore drilling, horizontal well placement, underground mining, and tunneling rely on accurate estimation of the in situ stresses. This study presents an integrated machine learning (ML) workflow for the reliable determination of the in situ stress in subsurface rock formations. The study workflow was completed in three phases. In the first phase, six supervised ML regression techniques were employed to develop the stress prediction models using laboratory true triaxial ultrasonic velocity tests (labTUV) data of subsurface rock samples retrieved from well 16A(78)‐32 located at the Utah FORGE geothermal site. In the second phase, subsurface geological formations were classified into rock facies using an unsupervised K‐means clustering algorithm. Finally, in the third phase, the optimized ML models were employed for predicting the in situ stresses in the corresponding rock facies in the well 16A(78)‐32 using field sonic logs. A comparison of evaluation metrics revealed the superior performance of ANN models for horizontal and vertical stress predictions with root mean squared error of 1.5, 0.6, and 1.7 MPa, and determination coefficient (R2) of 0.98, 0.98, and 0.96, for the testing/validation phases, respectively. The generalization capability of ML models was explored by uncovering the underlying physics. The mathematical expressions of constitutive relations were extracted from three ANN models. Further, a total of five rock facies were identified in the subsurface geological formations. The novel workflow would be capable of delivering reliable in situ stress profiles in subsurface geological formations without performing expensive field tests.

S-wave log construction through semi-supervised regression clustering using machine learning: A case study of North Sea fields

Accurate prediction of S-wave velocity from well logs is essential for understanding subsurface geological formations and hydrocarbon reservoirs. Machine learning techniques, including clustering and regression, have emerged as effective methods for indirectly estimating S-wave logs and other rock properties. In this study, we employed clustering algorithms to identify similarities among well log datasets, encompassing depth, sonic, porosity, neutron, and apparent density, facilitating the discovery of correlations among various wells. These identified correlations served as a foundation for predicting S-wave values using a novel semi-supervised approach. Our approach combined clustering, specifically k-means clustering, with different types of regressors, including Least Squares Regression (LSR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Our results demonstrate the superior performance of this integrated approach compared to traditional regression methods. We validated our methodology using various parametric and non-parametric regression techniques, showcasing its effectiveness not only on wells within the training region but also on wells outside the study area. We achieved a significant improvement in the R2 score metric, ranging from 2.22% to 6.51%, and a reduction in Mean Square Error (MSE) of at least 31% when compared to predictions made without clustering. This study underscores the potential of machine learning techniques for accurate prediction of S-wave velocity and other rock properties, thereby enhancing our comprehension of subsurface geology and hydrocarbon reservoirs.

Seismic Reservoir Characterization With Implicit Neural Representations

AbstractSeismic reservoir characterization refers to a set of techniques aimed at estimating static and/or dynamic physical properties of subsurface geological formations from seismic data, with applications ranging from exploration and development of energy resources, geothermal production, carbon capture and storage, and the assessment of geological hazards. Extracting quantitative information from seismic recordings, such as an acoustic impedance model, is however a highly ill‐posed inverse problem due to the band‐limited and noisy nature of the seismic data. As such, seismic inversion techniques strongly rely on additional prior information that penalizes (or promotes) some features in the recovered model. This paper introduces IntraSeismic, a novel hybrid seismic inversion method that combines implicit neural representations as an effective way to parameterize a subsurface model with the physics of the seismic modeling operator. We demonstrate its effectiveness in both pre‐stack and 3D/4D post‐stack seismic inversion using synthetic and field data sets. Key features of IntraSeismic are (a) unparalleled performance in 2D and 3D pre‐stack/post‐stack dynamic and static seismic inversion, (b) rapid convergence rates, (c) ability to seamlessly include hard constraints (i.e., well data) and perform uncertainty quantification, and (d) potential data compression and fast randomized access to specific portions of the inverted model.

A Numerical Study of Fault Reactivation Mechanisms in CO2 Storage.

Due to extensive industrialization and ongoing fossil fuel consumption, CO2 emissions have significantly contributed to climate change and rising greenhouse gas levels. The collection and storage of CO2 in subsurface geological formations have been proposed as a feasible alternative. The well-documented In Salah storage site is the subject of the chosen case study. For the numerical analysis, a two-dimensional finite element simulation of fault reactivation processes was conducted in the context of the CO2 storage. The goal of this research is to conduct a mechanistic numerical analysis of a typical CO2 storage condition. The selected analysis domain is 4 km (width) × 2 km (depth) and includes all important domains and formations (overburden, main caprock, lower caprock, and underburden) of the In Salah site. The simulation results indicate that the influence of fault reactivation under 32 MPa of base injection pressure results in peak vertical deformation of 0.044 m in the caprock and a vertical displacement magnitude of 0.015-0.020 m at the surface level (Z = 0 m). The derived vertical deformation findings at the surface level are in agreement with the data obtained from the in situ InSAR monitoring system in 2009. The effects of the changes in the fault dip angle, key caprock mechanical parameters, and in situ stress ratio on the displacement profile are evaluated within the parametric study. In comparison to the benchmark numerical run, the scenario ratio of k = 0.5 led to a significant reduction in the displacement. The simulation in which the fault dip angle was 30° produced a more pessimistic result with a larger displacement field. This could be an indication of heightened fault reactivation risks associated with low-angle faults in storage sites with strong horizontal stress regimes due to the combined effect of increased shear stress and reduced inherent frictional resistance on the fault plane. Considering that a vertical fault dip angle (90°) and an additional three 20 m long vertical fractures above the reservoir produced similar vertical displacement observed with the fault dipping at a 60° angle, this indicated that the vertical faults in the vicinity of the storage site pose limited safety risks to the integrity of the sealing rock.

Impact of Regional Pressure Dissipation on Carbon Capture and Storage Projects: A Comprehensive Review

Carbon capture and storage (CCS) is a critical technology for mitigating greenhouse gas emissions and combating climate change. CCS involves capturing CO2 emissions from industrial processes and power plants and injecting them deep underground for long-term storage. The success of CCS projects is influenced by various factors, including the regional pressure dissipation effects in subsurface geological formations. The safe and efficient operation of CCS projects depends on maintaining the pressure in the storage formation. Regional pressure dissipation, often resulting from the permeability and geomechanical properties of the storage site, can have significant effects on project integrity. This paper provides a state-of-art of the impact of regional pressure dissipation on CCS projects, highlights its effects, and discusses ongoing investigations in this area based on different case studies. The results corroborate the idea that the Sleipner project has considerable lateral hydraulic connectivity, which is evidenced by pressure increase ranging from <0.1 MPa in case of an uncompartmentalized reservoir to >1 MPa in case of substantial flow barriers. After five years of injection, pore pressures in the water leg of a gas reservoir have increased from 18 MPa to 30 MPa at Salah project, resulting in a 2 cm surface uplift. Furthermore, artificial CO2 injection was simulated numerically for 30 years timespan in the depleted oil reservoir of Jurong, located near the Huangqiao CO2-oil reservoir. The maximum amount of CO2 injected into a single well could reach 5.43 × 106 tons, potentially increasing the formation pressure by up to 9.5 MPa. In conclusion, regional pressure dissipation is a critical factor in the implementation of CCS projects. Its impact can affect project safety, efficiency, and environmental sustainability. Ongoing research and investigations are essential to improve our understanding of this phenomenon and develop strategies to mitigate its effects, ultimately advancing the success of CCS as a climate change mitigation solution.

Identification of Lithology from Well Log Data Using Machine Learning

INTRODUCTION: Reservoir characterisation and geomechanical modelling benefit significantly from diverse machine learning techniques, addressing complexities inherent in subsurface information. Accurate lithology identification is pivotal, furnishing crucial insights into subsurface geological formations. Lithology is pivotal in appraising hydrocarbon accumulation potential and optimising drilling strategies. OBJECTIVES: This study employs multiple machine learning models to discern lithology from the well-log data of the Volve Field. METHODS: The well log data of the Volve field comprises of 10,220 data points with diverse features influencing the target variable, lithology. The dataset encompasses four primary lithologies—sandstone, limestone, marl, and claystone—constituting a complex subsurface stratum. Lithology identification is framed as a classification problem, and four distinct ML algorithms are deployed to train and assess the models, partitioning the dataset into a 7:3 ratio for training and testing, respectively. RESULTS: The resulting confusion matrix indicates a close alignment between predicted and true labels. While all algorithms exhibit favourable performance, the decision tree algorithm demonstrates the highest efficacy, yielding an exceptional overall accuracy of 0.98. CONCLUSION: Notably, this model's training spans diverse wells within the same basin, showcasing its capability to predict lithology within intricate strata. Additionally, its robustness positions it as a potential tool for identifying other properties of rock formations.

Reservoir Static Modelling towards Safe CO2 Storage in Depleted Oil Reservoirs of ‘CRK’ Field, Niger Delta, Nigeria

Geological carbon storage (GCS) is gradually gaining acceptance as the technology of highest repute for the mitigation against greenhouse gas effect. This involves the injection and long-term or permanent storage of carbon dioxide (CO2) in subsurface geological formations. The development of a robust 3D reservoir model then becomes necessary to understand the facies changes and the petrophysical properties distribution of candidate CO2 storage reservoirs. This study attempts the use of static modelling technique to investigate the depleted oil and gas reservoirs of ‘CRK’ field in the Niger Delta sedimentary Basin as a potential CO2 storage site. Using an integrated approach of 3D seismic and a suit of well logs from the study area, two reservoir sands (D10C0 and D6200) were mapped. 3D static modelling of discrete and continuous property distribution of the reservoirs revealed the reservoirs are composed majorly of clean sands with water saturation ranging from 8 to 75%. The average porosity and permeability values are between 20 to 29% and 250 to 890mD respectively. A theoretical storage capacity of 24.85Mt is estimated for the two reservoirs combined, while the median effective storage capacity is 6.22Mt. Comparison of the results obtained in the study with recommended standard values show the reservoirs are suitable for CO2 storage. The property models constructed can serve as primary input for dynamic simulation of the oilfield in future studies.

Artificial intelligence-based prediction of hydrogen adsorption in various kerogen types: Implications for underground hydrogen storage and cleaner production

Hydrogen offers significant potential as a sustainable energy source. However, its storage and transportation pose challenges due to its volatility and low density. Subsurface geological formations, such as shale, sandstone, and coal, have been investigated as potential storage sites for hydrogen. Precise estimation of hydrogen adsorption in these formations necessitates a thorough understanding of kerogens, organic-rich sedimentary rocks prevalent in unconventional formations. In this study, we introduce a novel approach employing Extreme Gradient Boosting (XGBoost), Random forest (RF), Light gradient Boosting (LGBM), and Natural gradient boosting (NGBM) algorithms to intelligently predict hydrogen adsorption in various kerogen types. Among these, the NGBM algorithm, utilizing three input features (temperature, pressure, and kerogen types), demonstrated the highest predictive accuracy, achieving an R2 value of 0.989, RMSE of 0.062, and MAE of 0.037 for all data samples. SHAP diagram analysis identified kerogen types as the most influential parameter within the NGBM model. The presented approach has implications beyond energy storage, highlighting the significance of advanced technologies in addressing complex energy challenges. Precise hydrogen adsorption estimation in subsurface formations is vital for developing sustainable energy solutions, and our approach has the potential to expedite progress in this field. Interdisciplinary collaboration among geologists, chemists, and data scientists is crucial for devising innovative solutions for sustainable energy.

A comparative study on transport and interfacial physics of H2/CO2/CH4 interacting with H2O and/or silica by molecular dynamics simulation

Underground H2 storage (UHS), i.e., injecting H2 into subsurface geological formation and its withdrawal when needed, is identified as a promising solution for large-scale and long-term storage of H2. In this study, molecular dynamics (MD) simulation was performed at a typical temperature 320 K with pressure up to 60 MPa to predict H2 transport properties and H2–H2O–rock interfacial properties, which are compared with those of CO2 and CH4. The MD results show that the CH4 profiles of property variations with pressure lie between those of H2 and CO2 and more comparable to CO2. The interaction of H2 with H2O/silica is much weaker than that of CH4 and CO2. It is found that the effect of H2 pressure on altering the water contact angle and interfacial tension is negligible under all conditions. Unlike the multi-adsorption layers of the confined CO2 and CH4, there is only one adsorption layer of H2 confined by silica nano-slit. The planar diffusion of H2 in the confined system is slower than that in the bulk system at pressures lower than 20 MPa. The data and findings of this study will be useful for modeling the multiphase flow dynamics of UHS on reservoir scale, optimizing UHS operation, and assessing the performance of a cushion gas, e.g., CO2 or CH4.

Electrical Resistivity - Tomography Studies In Determining Shallow Aquifer Potential Zones a Case Study in Different Terrains

The study documents the effectiveness of 2 D (Two Dimensional Electrical Resistivity Survey (Tomography) to map shallow subsurface geological formations namely recent Alluvium, Gondwana deposits and hard rock deposits. 2 dimensional Electrical Resistivity Survey (or tomography) was conducted at 2 locations at Valarpuram and one location Madurantakam areas. Gondwana deposits mask Valarpuram Thandalam, villages (at Kancheepuram District, Tamilnadu). To the east of Valarpuram alluvium thickness increases at shallow depths. Weathered and hard granitic gneiss and hard charnockite rocks overlie topsoil in Madurantakam areas (Chengalpettu District, Tamilandu). The survey was conducted to 12 m depth to decipher shallow permeable zones. Gondwana formations consisting of clays, siltstones are predominant in Valarpuram Thandalam and surrounding areas. Hard rock formations are found in Madurantakam areas. 2 Dimensional Resistivity Survey results indicate 2-D sections with very low resistivity values in the range of 2 to 4 ohm-m in Valarpuram Thandalam areas indicating predominant clay deposits with poor ground water potential. To the east of Valarpuram Thandalam resistivity data is of moderately higher value indicating sand deposits at shallow depths. These are ideal locations for dug wells. Low to moderate values at shallow depths indicating weathered thickness up to 12 m are observed at Madurantakam location. From 12m depth steep rise in resistivity values are observed in hard indicating presence of massive rock from 12m depth and below areas. Thus, 2 D Resistivity Imaging Technique are helpful in delineating shallow aquifer potential and this in turn helps in deciding the depth of open wells with high precision.

Transient Pressure Interference during CO2 Injection in Saline Aquifers

Summary CO2 injection in subsurface geological formations (e.g., deep saline aquifers) causes pressure perturbations over a large area surrounding the injection well. Observation wells are widely considered in geologic CO2 storage (GCS) projects where the pressure perturbation induced by CO2 injection is measured. In this work, we use analytical and numerical modeling tools along with field data to examine the pressure behavior in GCS projects before and after CO2 arrival at an observation well. Before CO2 arrival, a baseline pressure trend is established which corresponds to single-phase brine flow across the observation well (approximated by the Theis solution). Therefore, analysis of early time pressure data is straightforward, provides the single-phase flow characteristics (mobility and storativity), and helps in establishing a baseline pressure change that can be extended beyond the single-phase flow period at the observation well. Upon CO2 arrival, a departure from this baseline trend is expected. For the pressure to detect the CO2 arrival at an observation well, the departure from baseline pressure behavior must be significant and well above the background noise levels. We use existing analytical models to determine the strength of the expected pressure departure signal from the baseline trend upon CO2 arrival. The strength of the expected pressure departure is found to be directly proportional to the change in the mobility upon CO2 arrival. Larger change in the flow mobility—compared with single-phase brine mobility—results in a stronger pressure departure signal. In addition, the departure is found to be upward (downward) from the baseline pressure trend when the mobility ratio is less (more) than unity. We present a pressure analysis approach through application to synthetic and field data and show the characteristic pressure behavior before and after CO2 arrival. We show that while generally the pressure can be either above or below the expected baseline pressure trend, it would be likely above the baseline upon CO2 arrival. This is because the mobility ratio becomes less than unity after CO2 arrival. We show that depending on the reservoir characteristics, changes in the pressure trend may or may not be sufficient to detect the CO2 arrival.

Delineation of geological formations and their influence on groundwater in Umunneochi L.G.A of Abia State and Its Environs

For socioeconomic growth and environmental wellbeing of the populace, the availability and sustainable management of groundwater resources is very crucial. Effective management and use of this invaluable resources requires an understanding of the subsurface geological formations and their impact on groundwater. This study was carried out in other to delineate the subsurface geological formations and evaluate their impact on groundwater availability in Umunneochi Local Government Area (L.G.A) of Abia State and its environs using Vertical Electrical Sounding (VES) analysis. The study includes gathering resistivity data via a network of VES surveys carried out at twelve key locations in six selected town within Umunneochi L.G.A. The field data were subsequently interpreted using an iteration software IP2WIN to identify various geological formations and evaluate their potential as aquifers, which substantially impact groundwater occurrence and distribution. The research findings showed distinct geological units with resistivity values ranging from 0.37 - 2000Ωm, which is indicating a variety of lithological properties, with sand and gravel layers showing higher permeability representing potential aquifers. The results of this study have significant implications for groundwater management in Umunneochi L.G.A. and its surroundings, and also provide recommendations for the strategic placement of boreholes and wells to maximize groundwater extraction and long-term use.

Effect of cyclic hysteretic multiphase flow on underground hydrogen storage: A numerical investigation

Hydrogen storage in subsurface geological formation is a complex process requiring information at multiple scales for successful understanding and implementation. While some laboratory investigations and pore-scale studies showed evidence of hydrogen and water cyclic hysteresis, field-scale underground hydrogen storage considering the cyclic hysteretic effect received little or no attention and thus remains poorly understood. This is particularly important due to the fact that an underground hydrogen storage project typically involves repeated cycles of hydrogen injection and withdrawal. In this work, numerical simulations were performed to examine the impact of the cyclic hysteretic effect on the dynamics of hydrogen saturation and the associated efficiency of storage and withdrawal cycles. The results indicate that the cyclic hysteretic effect impedes the injected hydrogen distribution in the formation and results in a greater ultimate hydrogen recovery factor during subsequent withdrawal phases (∼77%). However, the accumulated hydrogen in the formation top will, in turn, increase the formation pressure. The impact of capillary pressure and H2 dissolution in brine were also investigated. The results suggest that capillary pressure and dissolution could alter the hydrogen saturation distribution while depicting a negligible effect on the ultimate hydrogen recovery factor. It was found that the loss of hydrogen due to dissolution can be compensated by avoiding more significant residual trapping in our study.

Residual trapping of CO2, N2, and a CO2-N2 mixture in Indiana limestone using robust NMR coreflooding: Implications for CO2 geological storage

Carbon capture and sequestration (CCS) in geological formations is a prominent solution for reducing anthropogenic carbon emissions and mitigating climate change. The capillary trapping of CO2 is a primary trapping mechanism governed by the pressure difference between the wetting and nonwetting phases in a porous rock, making the latter a key input parameter for dynamic simulation models. During the CCS operational process, however, the CO2 is prone to contamination by impurities from various sources such as surfaces (e.g., pipelines and tanks) and the subsurface (e.g., existing natural gas). Such contamination can strongly influence the overall CO2 wettability, storage capacity, and containment security. Hence, the present study uses the nuclear magnetic resonance (NMR) core flooding technique to investigate and compare the residual saturations of pure CO2, pure N2, and a 50:50 CO2/N2 mixture in an Indiana limestone. The longitudinal and transverse relaxation times (T1 and T2) are measured to examine the displacement process of the pore network, and the trapping mechanism is evaluated at the pore scale as a determinant of the field-scale flow behavior. The NMR T1-T2 and 2D maps are used to observe the fluid configurations in the pore network, and the T1/T2 ratios are used to evaluate the microscopic wettability of the limestone grains by the pore-space fluids following each drainage/imbibition process step. The results indicate substantial residual gas trapping in the rock for the CO2-brine, N2-brine, and CO2/N2-brine systems, corresponding to gas saturations of 25%, 27%, and 26%, respectively. In the CO2-brine system, the intermolecular interplay between the CO2-enriched brine and limestone grains results in a higher T1/T2 ratio and significantly reduces the hydrophilicity of the limestone. Furthermore, the NMR T2 distribution reveals the occurrence of preferential water displacement into the large pores (r > 1 µm) and from the intermediate pores (0.03 µm < r < 1 µm), whereas water remains immobile in the smaller pores (r < 0.03 µm). The insignificant difference in residual trapping saturation between pure CO2 and the CO2-N2 mixture indicates the potential to allow for impurities in the CO2 phase in CCS without reducing the residual trapping capacity. Thus, the present work provides comprehensive information on the impact of gas injection on residual gas trapping in subsurface geological formations at the pore scale, thereby aiding in the development of CCS and other potential applications in enhanced oil recovery (EOR).

Molecular dynamics simulation of interfacial tension of the CO2-CH4-water and H2-CH4-water systems at the temperature of 300 K and 323 K and pressure up to 70 MPa

Subsurface geologic formations such as depleted hydrocarbon reservoirs, deep saline aquifers and shale formations have been considered promising targets for carbon dioxide and hydrogen storage. A solid understanding of the interfacial properties of multiphase systems, including binary (pure gas-water) and ternary (gas mixtures and water), is vital to assess for reliability and storage capacity of the geological formations. However, most previous experimental and simulation studies for interfacial properties have mainly focused on binary systems at low-medium pressure. Only a few experimental and simulation studies investigated the interfacial tension at high pressure (above 20 MPa) for the CO2-CH4-H2O system, and no simulation data are available for the H2-CH4-H2O system. In this study, Molecular dynamics simulations were used to predict the interfacial tension (γ) for both the binary and ternary system at 300 K and 323 K for a wide pressure range (1.0 to 70 MPa). The study was first conducted for the binary systems (H2O-CO2; H2O-CH4 and H2OH2) and then followed by the ternary systems (CO2-CH4-H2O and H2-CH4-H2O). The γ results were also validated with previous studies by comparing them to experimental and simulation data. The findings of this study indicated that γ data of binary and ternary systems decreased with increasing pressure and temperature. However, at high pressure (above 50 MPa), the γ data at 300 K and 323 K showed a plateau or changed very slightly, apparently not depending significantly on temperature. Furthermore, at a fixed pressure, determined γ values for the ternary system (H2-CH4-H2O) are constantly larger than for the CH4-H2O and CO2-CH4-H2O systems. The results provide extending or new γ data in simulation for the binary and ternary systems and contribute to evaluating the stability and long-term viability of various key Carbon Capture and Storage (CCS) and Underground Hydrocarbon Storage (UHS) related processes in support of the large-scale implementation of a hydrogen economy.

Feasibility of source-free DAS logging for next-generation borehole imaging

Characterizing and monitoring geologic formations around a borehole are crucial for energy and environmental applications. However, conventional wireline sonic logging usually cannot be used in high-temperature environments nor is the tool feasible for long-term monitoring. We introduce and evaluate the feasibility of a source-free distributed-acoustic-sensing (DAS) logging method based on borehole DAS ambient noise. Our new logging method provides a next-generation borehole imaging tool. The tool is source free because it uses ever-present ambient noises as sources and does not need a borehole sonic source that cannot be easily re-inserted into a borehole after well completion for time-lapse monitoring. The receivers of our source-free DAS logging tool are fiber optic cables cemented behind casing, enabling logging in harsh, high-temperature environments, and eliminating the receiver repeatability issue of conventional wireline sonic logging for time-lapse monitoring. We analyze a borehole DAS ambient noise dataset to obtain root-mean-squares (RMS) amplitudes and use these amplitudes to infer subsurface elastic properties. We find that the ambient noise RMS amplitudes correlate well with anomalies in conventional logging data. The source-free DAS logging tool can advance our ability to characterize and monitor subsurface geologic formations in an efficient and cost-effective manner, particularly in high-temperature environments such as geothermal reservoirs. Further validation of the source-free DAS logging method using other borehole DAS ambient noise data would enable the new logging tool for wider applications.

Structural analysis of microbiomes from salt caverns used for underground gas storage

Salt caverns are a safe and well-proven reservoir for large-scale natural gas storage and hence, a potential hydrogen storage. Contrary to natural gas, hydrogen is a favorable energy source for many microorganisms. Microorganisms are ubiquitously abundant in the upper lithosphere and therefore expected to be present also in subsurface geological formations potentially selected for H2 storage, such as salt caverns. Thus, future salt cavern hydrogen storage requires monitoring of the cavern microbiome, to evaluate and prevent unwanted microbial activities. In this study, we analyzed the microbiomes of brines sampled from the bottom of five German natural gas storage caverns. All brines were colonized by microorganisms in considerable cell numbers ranging from 2 × 106 cells ml−1 to 7 × 106 cells ml−1. The structures of the microbiomes were characterized by 16S rRNA gene amplicon sequencing. A core community detected in all five studied caverns consists of members affiliated to the Halobacteria, Halanaerobiales and Balneolales. Further, a phylotype belonging to the extremely halophilic, lithoautotrophic and sulfate-reducing genus Desulfovermiculus was found. Examination of microbial activity also included measuring hydrochemical parameters in order to assess the salt concentrations and the availability of nutrients and potential microbial carbon sources or metabolites. NaCl (4.7 M) was the main salt and sulfate (at average 40.8 mM) the main electron acceptor; methanol (up to 37.5 mM) and ethanol (up to 6.9 mM) were of anthropogenic origin and found in higher concentrations. Some putative microbial metabolites were found in lower concentrations (butyrate, ≤0.7 mM; formate, ≤0.08 mM; acetate, ≤0.5 mM; lactate, ≤0.06 mM); their potential relation to microbial activity is discussed. We propose a guideline for sampling and subsequent chemical and molecular biological analysis for future characterization of microbial communities of salt cavern brines.