Geologic Carbon Storage Research Articles

Decarbonizing the global steel industry in a resource-constrained future-a systems perspective.

Decarbonizing the global steel industry hinges on three key limited resources: geological carbon storage, zero-emission electricity and end-of-life scrap. Existing system analysis calls for an accelerated expansion of the supply of these resources to meet the assumed ever-increasing steel demand. In this study, we propose a different view on how to decarbonize the global steel industry, based on the principle that resource supply can only expand in line with historical trends and actual construction plans. Our analysis shows that global steel production cannot grow any further within a Paris-compatible carbon budget, resulting in a shortfall of approximately 30% against 2050 demand. This trajectory involves the phasing out of blast furnaces, along with strong growth in scrap recycling and hydrogen-based production. These findings highlight critical yet often overlooked challenges: (i) reducing excess demand while providing essential services, (ii) producing high-grade steel through upcycling scrap, and (iii) ensuring an equitable distribution of limited production across the globe. These perspectives contrast with those of the current agenda, which largely emphasizes the need to invest in new production technologies. Grounded in a physical basis, this analysis offers a complementary perspective for a more balanced debate in policymaking and industrial strategy. This article is part of the discussion meeting issue 'Sustainable metals: science and systems'.

Evaluation of CO2 storage potential of Carboniferous sandstones in the Maritime Provinces of Canada

The suitability of Carboniferous sandstones in three regions of the Maritime Provinces of Canada for geological carbon storage was evaluated: the Horton Bluff Formation in the Windsor Sub-basin, the lower Cumberland Group sandstones in the Cumberland–Sackville Sub-basin, and the Pennsylvanian sandstones of Prince Edward Island. the properties of potential reservoirs and characteristics of vertical seals and barriers to lateral migration were evaluated using previously collected well logs, sample descriptions, core analyzes and seismic interpretations. Reservoir quality was found to be the limiting factor in all three regions. Sandstones in the upper Hurd Creek Member of the Horton Bluff Formation locally have porosities up to 15% and permeabilities up to 25 milliDarcies at depths up to 1200 m. their aggregate thickness may be suitable for GCS, but individual sandstones are thin and likely of limited lateral extent. the lower Cumberland Group contains sand-dominated successions up to 1 km thick with low porosity (5–7%) where known in the subsurface. Sandstone bodies in the Bradelle, Green Gables, and Cable Head formations beneath Prince Edward Island exceed tens of meters in thickness with porosities averaging up to 10–12% and permeabilities up to 10 milliDarcies. Evaporites in the overlying Windsor Group would provide a suitable seal for the Horton Bluff Formation; in other areas the top seal would be provided by mud-prone heterolithic intervals. the evaluated areas may provide opportunities for small onshore storage projects. Further work is warranted to delineate reservoir trends and verify the integrity of potential top seals and traps.

Multiscale simulation of water/oil displacement with dissolved CO[formula omitted]: Implications for geological carbon storage and CO[formula omitted]-enhanced oil recovery

This study combines molecular and pore-scale simulations to enhance our understanding of water, carbon dioxide (CO2), and oil flow in a porous system for high-efficiency geological carbon storage and enhanced oil recovery. We use molecular dynamics simulations to study the variation of interfacial tension (IFT), viscosity, and contact angle across varying system CO2 concentrations. The study reveals that the IFT simulation accuracy in the three-component mixture system relates positively to the volume-to-interfacial area ratio, and IFT decreases with CO2 concentration. Oil–CO2 mixture viscosity decreases and shows an intersection with water viscosity as CO2 concentration increases. The contact angle on the hydrophobic surface of kaolinite first increases and then decreases with CO2 concentration, while the hydrophilic surface contact angle is not sensitive to the CO2 concentration. Visualizing CO2 density distribution reveals its accumulation at fluid-fluid and fluid-solid interfaces and the combined effect results in the highest CO2 density at contact-line regions. Meanwhile, the elevated CO2 density at fluid-fluid interfaces reduces IFT, contributing to the initial contact angle increase, while the CO2 density increase at fluids-solid surface explains the subsequent contact angle decrease. These findings are then upscaled into a core-scale system using lattice Boltzmann simulations. The results are summarized into a phase diagram, which reveals the non-monotonic variations of the initial-residual (I-R) curves across different CO2 concentrations, and the residual saturation overall decreases with CO2 concentration for high initial saturations. These findings underscore the critical roles of system CO2 concentration and rock components, particularly kaolinite clay minerals, in subsurface multiphase seepage confronted in geological carbon storage and CO2-enhanced oil recovery, highlighting the importance of digital rock physical chemistry.

Deep learning-based geological parameterization for history matching CO2 plume migration in complex aquifers

History matching is crucial for reliable numerical simulation of geological carbon storage (GCS) in deep subsurface aquifers. This study focuses on inferring highly complex aquifer permeability fields with multi- and intra-facies heterogeneity to improve the characterization of CO2 plume migration. We propose a deep learning (DL)-based parameterization strategy combined with the ensemble smoother with multiple data assimilation (ESMDA) algorithm to formulate an integrated inverse framework. The DL model is employed to parameterize non-Gaussian permeability fields using low-dimensional latent variables in a Gaussian distribution, thereby mitigating the non-Gaussianity issue faced by the ensemble-based ESMDA inverse method and simultaneously alleviating the computational burden of high-dimensional inversion. The efficacy of the integrated DL-ESMDA inverse framework is demonstrated using a 3-D GCS model, where it estimates the non-Gaussian permeability field characterized by multi- and intra-facies heterogeneity. Results show that the DL model is able to represent the highly complex and high-dimensional permeability fields using low-dimensional latent vectors. The DL-ESMDA framework sequentially updates these low-dimensional latent vectors instead of the original high-dimensional permeability field to obtain posterior estimations of the permeability field. The resulting CO2 plume migration closely matches historical measurements, suggesting a significantly improved model reliability after history matching. Additionally, a substantial reduction in uncertainty for future plume migration predictions beyond the history matching period is observed. The proposed framework provides an effective approach for reliable characterization of CO2 plume migration in highly heterogeneous aquifers, enhancing GCS project operation and risk analysis.

An Analytical Formulation for Correcting the Relative Permeability of Gas‐Water Flow in Propped Fractures Considering the Effect of Brinkman Flow

AbstractGas‐water flow in propped fractures can be commonly observed in various practical applications, including hydrocarbon development, geothermal exploitation, contaminant transport, and geological carbon storage. The fluid flow in a propped fracture can be regarded as Darcy type if only the resistance from the propping materials (e.g., cement in natural fractures and proppant‐pack in hydraulic fractures) accounts. However, if the fracture width is sufficiently small, the extra resistance from the viscous shear of fracture walls cannot be neglected, resulting in the appearance of Brinkman flow. In practice, during the development of a naturally fractured aquifer or a hydraulicly fractured hydrocarbon reservoir, the fracture width will be significantly reduced as the production proceeds. Therefore, the Brinkman flow can impose a strong effect on the fluid transportation within the fractures. However, the existing study about the two‐phase Brinkman flow in propped fractures is still far from adequate. In this work, on the basis of a modified two‐phase Brinkman equation, the authors derive a novel analytical formulation to correct the relative permeability of gas‐water two‐phase flow in propped fractures to account for the Brinkman effect. With the aid of the proposed formulation, the authors carry out a comprehensive investigation of the influence of Brinkman flow on the effective gas‐water relative permeability and well performance. The calculated results show that the effect of Brinkman flow on the water phase (or gas phase) transportation is more significant with a larger water (or gas) saturation. A narrower propped fracture is more likely to induce Brinkman flow, thus leading to a lower relative permeability for both water and gas phases. As the propping‐material permeability is increased, the fluid transportation bears more severe viscous drag from fracture walls, and the relative permeability will be consequently reduced. Only if the fracture width is significantly reduced during the production, the Brinkman flow demonstrates its influence on the well performance. Otherwise, Darcy's law can provide sufficiently accurate results in characterizing the two‐phase flow in propped fractures.

Unveil the controls on CO2 diffusivity in saline brines for geological carbon storage

Unveil the controls on CO2 diffusivity in saline brines for geological carbon storage

Risk assessment and management strategy of geologic carbon storage in multi-well site

Many oil and gas production areas may serve as potential geologic carbon storage(GCS) sites, but large-scale CO2 injection in these sites with multiple legacy wells poses a leakage risk. Therefore, it is crucial to analyze the well leakage risk of these GCS sites and develop effective risk management strategies to minimize CO2 and brine leakage. In this study, we hypothesized a GCS site based on the Jingbian area in the Ordos Basin, which has 177 legacy wells. We conducted a three-dimensional geological modeling of the site and simulated the injection and migration of CO2. Using the National Risk Assessment Partnership’s open-source Integrated Assessment toolkit, we outlined a workflow for assessing the risk of CO2 and brine leakage in multi-well site. Simulations of CO2 and brine leakage through 177 existing wells into the USDW or the atmosphere are used as an indicator of potential risk. Finally, we proposed risk management strategies based on well integrity, injection rate, and pollution range, verifying their effectiveness. The results of the 95th percentile (P95) model implementation showed that the leakage risk of GCS sites was low over 150 years, with a CO2 leakage of 15,996.4 tonnes(0.16 % of the 107 t injected). The risk-based strategy outperformed both distance-based strategy and hybrid strategy overall. Reducing injection rates alone proved ineffective as a risk management strategy. Only high-risk wells near the injection zone may cause pollution to USDW. Meanwhile, legacy wells have negligible impact on atmospheric pollution. The management strategy based on pollution range can be used as a supplement to the risk-based strategy in practical engineering.

The impact of regional resources and technology availability on carbon dioxide removal potential in the United States

Abstract To achieve net zero carbon emissions by mid-century, the United States may need to rely on carbon dioxide removal (CDR) to offset emissions from difficult-to-decarbonize sectors and/or shortfalls in near-term mitigation efforts. CDR can be delivered using many approaches with different requirements for land, water, geologic carbon storage capacity, energy, and other resources. The availability of these resources varies by region in the U.S. suggesting that CDR deployment will be uneven across the country. Using the Global Change Analysis Model for the United States (GCAM-USA), we modeled six classes of CDR and explored their potential using four scenarios: a scenario where all the CDR pathways are available (Full Portfolio), a scenario with restricted geologic carbon storage (Low CCS), a scenario where the availability of bio-based CDR options is limited (Low Bio), and a scenario with constraints on enhanced rock weathering (ERW) capabilities (Low ERW). We find that by employing a diverse set of CDR approaches, the U.S. could remove between 1 and 1.9 GtCO2/yr by midcentury. In the Full Portfolio scenario, direct air carbon capture and storage (DACCS) predominates, delivering approximately 50% of CO2 removal, with bioenergy with carbon capture and storage (BECCS) contributing 25%, and ERW delivering 11.5%. Texas and the agricultural Midwest lead in CDR deployment due to their abundant agricultural land and geological storage availability. In the Low CCS scenario, reliance on DACCS decreases, easing pressure on energy systems but increasing pressure on the land. In all cases CDR deployment was found to drive important impacts on energy, land, or materials supply chains (for example for ERW) and these effects were generally more pronounced when fewer CDR technologies were available.

Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models

Machine learning-based Surrogate Reservoir Models (SRMs) can replace/augment multi-physics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. This research will demonstrate SRMs’ potential in long-term simulations and optimization of geological carbon storage in a real-world geological setting and address challenges in big data curation and model training. The present study focuses on CO2 storage in the Smeaheia saline aquifer. Two SRMs were created using Deep Neural Networks (DNNs) to predict CO2 saturation and pressure over all grid blocks for 50 years. 18 million samples and 31 features, including reservoir static and dynamic properties, build the input data. Models comprise 3–5 hidden layers with 128–512 units apiece. SRMs showed a runtime improvement of 300 times and an accuracy of 99% compared to the 3D numerical simulator. The genetic algorithm was then employed to determine the optimal rate and duration of CO2 injection, which maximizes the volume of injected CO2 while ensuring storage operations’ safety through constraints. The optimization continued for the reproduction of 100 generations, each containing 100 individuals, without any hyperparameter tuning. Finally, the optimization results confirm the significant potential of Smeaheia for storing 170 Mt CO2.

Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning

Identifying the time-varying control schemes that maximize storage performance is critical to the commercial deployment of geological carbon storage (GCS) projects. However, the optimization process typically demands extensive resource-intensive simulation evaluations, which poses significant computational challenges and practical limitations. In this study, we presented the multimodal latent dynamic (MLD) model, a novel deep learning framework for fast flow prediction and well control optimization in GCS operations. The MLD model implicitly characterizes the forward compositional simulation process through three components: a representation module that learns compressed latent representations of the system, a transition module that approximates the evolution of the system states in the low-dimensional latent space, and a prediction module that forecasts the flow responses for given well controls. A novel model training strategy combining a regression loss and a joint-embedding consistency loss was introduced to jointly optimize the three modules, which enhances the temporal consistency of the learned representations and ensures multi-step prediction accuracy. Unlike most existing deep learning models designed for systems with specific parameters, the MLD model supports arbitrary input modalities, thereby enabling comprehensive consideration of interactions between diverse types of data, including dynamic state variables, static spatial system parameters, rock and fluid properties, as well as external well settings. Since the MLD model mirrors the structure of a Markov decision process (MDP) that computes state transitions and rewards (i.e., economic calculation for flow responses) for given states and actions, it can serve as an interactive environment to train deep reinforcement learning agents. Specifically, the soft actor-critic (SAC) algorithm was employed to learn an optimal control policy that maximizes the net present value (NPV) from the experiences gained by continuous interactions with the MLD model. The efficacy of the proposed approach was first compared against commonly used simulation-based evolutionary algorithm and surrogate-assisted evolutionary algorithm on a deterministic GCS optimization case, showing that the proposed approach achieves the highest NPV, while reducing the required computational resources by more than 60%. The framework was further applied to the generalizable GCS optimization case. The results indicate that the trained agent is capable of harnessing the knowledge learned from previous scenarios to provide improved decisions for newly encountered scenarios, demonstrating promising generalization performance.

Boosting Barlow Twins Reduced Order Modeling for Machine Learning‐Based Surrogate Models in Multiphase Flow Problems

AbstractWe present an innovative approach called boosting Barlow Twins reduced order modeling (BBT‐ROM) to enhance the reliability of machine learning surrogate models for multiphase flow problems. BBT‐ROM builds upon Barlow Twins reduced order modeling that leverages self‐supervised learning to effectively handle linear and nonlinear manifolds by constructing well‐structured latent spaces of input parameters and output quantities. To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT‐ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. To evaluate the performance of BBT‐ROM, we conduct three three‐dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. The results demonstrate that BBT‐ROM excels at predicting non‐wetting phase saturation (e.g., oil or saturation) and fluid pressure, with average relative errors ranging from 0.5% to 3%. Importantly, BBT‐ROM showcases robustness when faced with limited input parameter space during GCS testing.

Techno-economic-environmental study of CO2 and aqueous formate solution injection for geologic carbon storage and enhanced oil recovery

As carbon capture, utilization, and storage (CCUS), carbon-dioxide enhanced oil recovery (CO2 EOR) has inherent shortcomings, such as inefficient oil recovery and carbon storage, and low storage security with mobile CO2. This paper presents a techno-economic-environmental analysis of using formate species, a product of CO2 electrochemical reduction, as an alternative carbon carrier for sequestration and EOR in a carbonate oil reservoir in the Gulf of Mexico Basin. CO2 injection, water-alternating-CO2 injection, and aqueous formate solution injection were compared using a compositional reservoir simulation model and an economic calculator. Formate solution injection yielded greater levels of oil recovery and net carbon storage, where the carbon-bearing species resided in the dense aqueous phase without having to rely on petrophysical trapping mechanisms (structural and capillary). The enhanced oil production, net carbon storage, and storage security can be promoted by providing formate-based CCUS with more incentives (e.g., greater tax credit) in comparison to CO2-based CCUS for EOR and the manufacture of chemicals and products. In establishing carbon storage incentive policies and regulations, policymakers should include alternative carbon carriers.

Stochastic control of geological carbon storage operations using geophysical monitoring and deep reinforcement learning

Geological carbon storage (GCS) is the process of injecting and storing carbon dioxide (CO2) in the subsurface to reduce greenhouse gas emissions. Safe and profitable GCS operations require effective decision-making in the presence of uncertain geological models, a process which can often be facilitated with geophysical monitoring. In this study, we examine how sequential decision-making algorithms can be combined with geophysical measurements for the optimal control of GCS operations. Specifically, we develop an artificial intelligence model using deep reinforcement learning (DRL) that takes geophysical time-lapse gravity and well pressure monitoring data as input and delivers an optimal CO2 injection policy. The objective of the problem at hand is to maximize the profit of a hypothetical GCS operation while mitigating the potential for induced seismicity, by training DRL agents using combined geostatistical, reservoir and geophysical simulation. Comparisons against two benchmarks – a constant injection strategy and an injection schedule optimized using a commercial reservoir simulator toolbox – show that the stochastic control of such operations from subsurface monitoring data using deep reinforcement learning is feasible. Evaluation results show that DRL agent behavior generates profits which are on average 1 to 8 percent higher than what is possible through a constant injection approach. Furthermore, we show that DRL can generate optimal injection policies applicable to the true (yet previously unseen) subsurface given carefully managed levels of uncertainty.

A numerical model for evaluating the long-term migration and phase transition behavior of foam-assisted injection of CO2 in saline aquifers

Geological Carbon Storage (GCS) is a rapidly developing technology with the potential to mitigate the environmental impact of excessive greenhouse gas emissions. Foam-assisted injection of CO2 can effectively alter fluid rheological properties, thereby enhancing sweep efficiency. In this paper, a numerical model to evaluate the long-term migration behavior of foam-assisted injected CO2 in saline aquifers is developed. It is found that foam-assisted injection improves storage efficiency and reduces the plume migration distance compared to direct CO2 injection. This creates higher demands on the injection pressure but minimizes the risk of leakage. The effects of injection strategy, volume fraction of foam, and reservoir heterogeneity were investigated. Modeling results show that the injection strategy with a high foam volume fraction enhances the sweep efficiency and improves the transition rate of injected CO2 to the dissolved state. This can effectively enhance the long-term security of the sequestration. Simulations of layered formations indicates that foams can potentially inhibit the rapid migration of plumes along high-permeability zones. Our study provides insights for foam-assisted injection to enhance reservoir storage efficiency and long-term safety for GCS.

A Statistical Analysis of Fluid Interface Fluctuations: Exploring the Role of Viscosity Ratio.

Understanding multiphase flow through porous media is integral to geologic carbon storage or hydrogen storage. The current modelling framework assumes each fluid present in the subsurface flows in its own continuously connected pathway. The restriction in flow caused by the presence of another fluid is modelled using relative permeability functions. However, dynamic fluid interfaces have been observed in experimental data, and these are not accounted for in relative permeability functions. In this work, we explore the occurrence of fluid fluctuations in the context of sizes, locations, and frequencies by altering the viscosity ratio for two-phase flow. We see that the fluctuations alter the connectivity of the fluid phases, which, in turn, influences the relative permeability of the fluid phases present.

Optimizing Injection Well Trajectory to Maximize CO2 Storage Security and Minimize Geomechanical Risk

Summary The work demonstrates an optimal well design for a potential geological carbon storage (GCS) project in Kern County, California (USA). Carbon dioxide (CO2) plume shape, size, and pressure response history in the subsurface are outcomes. We created a toolbox (pyCCUS) to standardize the well design optimization process and it is applicable to different carbon storage assets. This toolbox is helpful to maximize storage security and minimize geomechanical risk. The numerical model of the storage formation features two-way coupled transport and geomechanical deformation. It honors a predefined injection scheme with injection rates that ramp up and then decline for a total of 12.3 MtCO2 injection in 18 years. The peak injection rate is greater than 1 MtCO2/yr, whereas the post-injection monitoring period is 100 years. We propose to develop a long, deviated injection well to best address the injectivity and plume migration challenges for this complex, heterogeneous, dipping formation. The chosen well trajectory improves injectivity while minimizing formation pressure buildup. The well design optimization successfully reduces the pressure buildup by 54% over the base design while only increasing the areal extent of the plume by 21%. We quantify the CO2 plume shape and size at the land surface. The plume grows rapidly during injection, but it increases only slightly after shut-in due to slow updip migration driven by buoyancy. The plume becomes stationary within the post-injection monitoring period. The optimal injector design balances the optimization goals of CO2 plume size, pressure increase, and pressure buildup at geological faults. The optimal injection well design is robust under uncertainties from injection schemes and geological model realizations. Rock deformation due to the pressure buildup is also computed. The model estimates 2.1 cm of uplift that occurs during the year of the peak annual injection rate. Land surface uplift strongly correlates with the subsurface pressure response.

Competitive sorption of CH₄ and CO₂ on coals: Implications for carbon geo-storage

Geological carbon storage, particularly within coal seams, is recognized as a viable strategy for achieving net-zero emissions. However, following CO₂ injection into the coal seam, limited studies have addressed the competitive sorption dynamics of CH₄ and CO₂ on coal, despite the natural presence of CH₄ in these formations. In this work, sorption experiments were conducted using two types of coal: sub-bituminous coal and anthracite. Initially, pure CH4 and CO2 gases were employed to conduct individual sorption tests. Subsequently, the competitive sorption of CH4 and CO2 was evaluated using pre-mixed binary gas mixtures with varying CH4/CO2 ratios. Further, the displacement effect of CO2 on CH4 was investigated by injecting CO2 into coal samples that had been pre-adsorbed with CH4. The data reveal that, for both coals, the ideal selectivity calculated from pure gas measurements underestimates the corresponding values from real multicomponent systems. Anthracite demonstrates a higher selectivity for CO₂ over CH₄ during both adsorption and desorption processes when compared to the ideal selectivity calculated from pure gas sorption data. Conversely, the sub-bituminous coal initially shows lower selectivity for CO₂ than for CH₄ during adsorption, but this trend reverses and intensifies during desorption. If competitive sorption effects are neglected, coal selectivity for CO₂ over CH₄ under the examined conditions would be underestimated by a factor of 1.5 to 2.5. Due to the competitive sorption effects between CH₄ and CO₂, the Langmuir equilibrium constants for gas mixtures are influenced by compositional changes, leading to dynamic deviations from those observed under pure gas conditions. This finding contrasts with the traditional extended Langmuir model, which presumes that the equilibrium constants remain unchanged regardless of gas composition. In addition, the displacement study underscores the efficacy of CO2 in displacing CH4, with higher CO2 ratios intensifying displacement effects. The study highlights the substantial impact of real multicomponent scenarios on coal selectivity for CO₂ and CH₄, offering deeper insights into predicting competitive sorption behavior between CO₂ and CH₄ during CO₂-enhanced coalbed methane recovery and carbon storage processes.

Enhanced oil recovery in tight reservoirs by ultrasonic-assisted CO2 flooding: Experimental study and molecular dynamics simulation

CO2-enhanced oil recovery (CO2-EOR) has attracted great attention due to its dual effects of geological carbon storage and economic benefits, but the clay swelling, asphaltene deposition and gas channeling induced by CO2 still restrict its field application. The employment of chemical agents for the purpose of improving CO2-EOR performance is often limited by the heterogeneity of reservoirs and the potential for formation damage. Physical methods, such as ultrasonic-assisted CO2 flooding, appear to offer a promising alternative for CO2-EOR and geological sequestration. This study has conducted oil displacement experiments to investigate the EOR potential of ultrasonic-assisted CO2 flooding in tight reservoirs and clarify the effects of different ultrasound parameters. The ultrasonic waves with 20 kHz of acoustic frequency, 30 W/cm2 of sound intensity, 30 min of sonication time and moderate intermittent working mode can yield violent cavitation effects and avoid undesirable energy dissipation. Nearly a quarter of the saturated crude oil can be recovered from the tight core through ultrasonic-assisted CO2 flooding. It can be attributed to the composition alterations of crude oil and property changes of core matrix. Coupling with ultrasonic waves, CO2 would extract and vaporize crude oil more easily, which promotes oil swelling, lowers oil density and viscosity, decreases oil–gas interfacial tension and thus improves oil recovery. Besides, the ultrasound can help to mitigate the impact of CO2 adsorption-induced clay swelling on pore sizes through fluctuating pressure. And the strong hydrodynamic shear forces generated by mechanical vibration of ultrasound may also help to connect individual small pores and extend micro-fractures. These synergistic effects result in the transformation of micropores into mesopores and macropores. Furthermore, the wettability alteration of core samples implies that the ultrasonic-assisted CO2 flooding can induce the detachment of oil films from the rock surface. Finally, the results of molecular dynamics simulations indicate that the improvement in pressure inside the core sample resulting from ultrasonic cavitation facilitates the mixing of CO2 and oil, thereby weakening gas channeling and improving the displacement efficiency.

An assessment of controlled source EM for monitoring subsurface CO2 injection at the wyoming carbonSAFE geologic carbon storage site

We evaluate if electromagnetic (EM) geophysical methods for monitoring geologic carbon storage (GCS) efforts at the Wyoming CarbonSAFE project adjacent to the Dry Fork Station power plant near Gillette, Wyoming. This first involved acquiring both electric and magnetic fields at eleven different locations ranging in distance from immediately adjacent to 4 km from the plant. Passive EM measurements were made to provide spectral EM noise measurements generated by electricity production at the plant and to determine if useful magnetotelluric (MT) data can be successfully collected in the region. The processed data indicate that useful MT data can be collected as long as the site is located more than 2km away from the power plant as well as active roads and rail lines. Controlled source EM data were collected using three different source configurations, two of which connected to steel casings used to complete the injection wells. Comparing the EM noise measurements to the CSEM data show measurable electric and magnetic field signals at all sites. Next a series of three-dimensional (3D) numerical models were built that simulate resistivity changes caused by the proposed CO2 injection at depths ranging from 2.4 to 3.0km. These models were used to simulate various EM measurement configurations. The modeling shows that casing-source CSEM monitoring can provide sensitivity to the injected CO2 if source electrodes are connected to the bottom of one or both of the injection wells.

Estimation of CO2-Brine Interfacial Tension Based on an Advanced Intelligent Algorithm Model: Application for Carbon Saline Aquifer Sequestration.

The emission reduction of the main greenhouse gas, CO2, can be achieved via carbon capture, utilization, and storage (CCUS) technology. Geological carbon storage (GCS) projects, especially CO2 storage in deep saline aquifers, are the most promising methods for meeting the net zero emission goal. The safety and efficiency of CO2 saline aquifer storage are primarily controlled by structural and capillary trapping, which are significantly influenced by the interactions between fluid and solid phases in terms of the interfacial tension (IFT) between the injected CO2 and brine at the reservoir site. In this study, a model based on the random forest (RF) model and the Bayesian optimization (BO) algorithm was developed to estimate the IFT between the pure and impure gas-brine binary systems for application to CO2 saline aquifer sequestration. Then three heuristic algorithms were applied to validate the accuracy and efficiency of the established model. The results of this study indicate that among the four mixed models, the Bayesian optimized random forest model fits the experimental data with the smallest root-mean-square error (RMSE = 1.7705) and mean absolute percentage error (MAPE = 2.0687%) and a high coefficient of determination (R2 = 0.9729). Then the IFT values predicted via this model were used as an input parameter to estimate the CO2 sequestration capacity of saline aquifers at different depths in the Tarim Basin of Xinjiang, China. The burial depth had a limited influence on the CO2 storage capacity.