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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A Viable Path Forward to Carbon Capture and Storage in Wyoming, USA

The State of Wyoming, USA, benefits from favorable geologic, regulatory, and infrastructure attributes that promote efficient and sustainable carbon capture and storage (CCS). Two research sites in Wyoming exhibit high potential for the underground geologic storage of greater than 50 million metric tons of carbon dioxide (CO 2 ) over 25 years. Funded by the U.S. Department of Energy’s (DOE) Carbon Storage Assurance and Facility Enterprise (DOE CarbonSAFE) Phase I prefeasibility initiative, studies of both sites indicate promising capture, transport, utilization, and storage characteristics. The Dry Fork site is located near Gillette, in northeast Wyoming. Four storage reservoir/seal pairs have been identified at the Dry Fork site that provide excellent prospects for stacked storage of CO 2 . All four reservoirs lie at sufficient depths to maintain supercritical CO 2 and all four seals exhibit favorable characteristics for stratigraphic confinement. The reservoir with the highest expected significance is the Pennsylvanian/Permian Minnelusa Formation, which consists of near-shore dunes and shoreline sands, as well as shale and carbonate layers. The Minnelusa resides at a depth of ~2,900 m below the land surface and is ~45 m thick in the study area. Average porosity for samples from core within 10 km of the study area is 9%, with permeability values as high as 169 mD (n=6). The Permian Opeche Formation seals the Minnelusa and consists of redbed shales with minor fine-grained siltstones and evaporite deposits. Entry pressures as high as ~4700 psia were measured by mercury injection capillary pressure (MICP) analysis on core samples from the Opeche. A CarbonSAFE Phase II feasibility proposal at the Dry Fork site has recently been selected by DOE for negotiation leading to award. The second proposed site is located at the Rock Springs Uplift (RSU) in southwest Wyoming. Four reservoir/seal pairs have been studied at the RSU, all of which present promising geologic attributes for the stacked storage of CO 2 . The two deepest reservoirs (Mississippian Madison Limestone and Pennsylvanian/Permian Weber Formation) and their seals were studied previously under the auspices of the Wyoming Carbon Underground Storage Project (WY-CUSP), which was funded by DOE and the State of Wyoming. For the more recent CarbonSAFE initiative, two shallower reservoir/seal pairs were studied in detail. The highest priority of these reservoirs is the Triassic/Jurassic Nugget Formation, which consists of eolian sands and minor fluvial and interdune deposits. The Nugget lies at a depth of ~2,800 m and is ~140 m thick in the study area. Average measured porosity for samples from core within 40 km of the study site is 9%, with permeability values as high as 91 mD (n=10). The Nugget is sealed by the overlying Jurassic Gypsum Spring and Morrison Formations, as well as a thick (~1,700 m) regional Upper Cretaceous seal that includes shales from multiple geologic units. Entry pressures as high as 1120 psia were measured by MICP for shales within this regional confining unit. Overall, the State of Wyoming provides an advantageous climate to develop sustainable integrated CCS projects based on the following considerations: (1) well-characterized and favorable geology; (2) some of the largest deposits of economically recoverable coal in the USA; (3) an existing CO 2 -enhanced oil recovery industry, including CO 2 pipeline infrastructure; (4) supportive commercial enterprises, including utilities and the oil and gas industry; (5) supportive policymakers; (6) a favorable legal and regulatory environment; (7) proximity to carbon-constrained markets such as the State of California; (8) large CO 2 emission sources, including coal-fired power plants; and (9) backing for carbon capture, storage, and management related research and development, specifically implementation of the Integrated Test Center at Dry Fork Station, which is hosting the coal-track of the NRG/COSIA Carbon XPrize® competition.

Phillips, Erin↗
DOE OSTI2020

Fibre-optic based DAS technology for Long-term Seismic Monitoring of Carbon Capture and Storage Projects

Long-term seismic monitoring of carbon capture and storage projects (CCS) is needed to verify that the injected gas is safely stored in the subsurface until permanence can be assured. Conventional surface seismic monitoring techniques are usually expensive, require highly invasive surface operations, and need significant time investments on the part of personnel for both the field effort and processing the acquired data. For these reasons, permanent reservoir monitoring (PRM) technologies are preferred, as they can offer a cost-effective solution for long-term monitoring. In this context, we have developed and trialed the use of distributed acoustic sensing (DAS) coupled to permanent rotary sources, called surface orbital vibrators (SOV), with the objective to build a continuous monitoring array for CCS projects.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Promoting Domestic and International Consensus - Program Area 1: Carbon Capture & Storage & Clean Energy Systems

This is the Final Report for the U.S. Department of Energy Cooperative Agreement DE-FE-0024159, Promoting Domestic and International Consensus on Fossil Energy Technologies: Carbon Capture and Storage and Clean Energy Systems carried out by the United States Energy Association. It is a compendium of each quarterly report submitted over the 7-year period of the agreement accounting for all accomplishments, including major activities, significant results, major findings or conclusions, key outcomes or other achievements. Changes in approach or aims, and reasons for those changes, are included as well as any problems or delays and actions taken and planned to resolve them. It also summarizes budget statuses and any changes in key personnel throughout the life of the agreement. Originally planned for 5 years, the agreement was extended twice through back-to-back no-cost extensions, one to enable full completion of tasks critical to the program’s success and the other due to the Covid-19 Pandemic. The goals of the program were to provide increased knowledge regarding Carbon, Capture, and Storage (CCS) and Clean Energy Systems (CES) to Industry and Government decision makers, technology developers, educators, policymakers, environmental and other stakeholders, and the public by sharing research, technologies, and best practices with domestic and international partners. Increasing such knowledge was key to the other goals of the program of building of a broad consensus among domestic and international decision makers, stakeholders, and the public to take action to support and implement CCS and CES technologies and systems to ensure clean, secure, and affordable energy while enhancing environmental protection. The program accomplished these goals through a series of activities including hosting of conferences and workshops, in-person briefings, virtual webinars, reports and white papers, and keeping stakeholders informed of industry happenings through email distribution lists. It is the belief of the authors of this report, that these activities can be considered to have been successful in their goals.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Techno-Economic Evaluation of Strategies to Approach Net-Zero Carbon Sustainable Aviation Fuel via Woody Biomass Gasification and Fischer-Tropsch Synthesis

Reducing the carbon dioxide (CO2) emissions from the transportation industry is a key target for achieving global net-zero carbon goals. Wide-spread electrification, efficient engine design, and alternative fuel implementations have been introduced for light-duty vehicles and are projected to significantly reduce light-duty emissions in the near future. Conversely, the aviation sector contributes considerably towards transportation-based carbon emissions, but current projections do not show substantial reductions in carbon emissions over time. Challenges for the aviation sector include low compatibility with electrification, relative inflexibility to variations in fuel properties, and requirement of high energy density fuels. Thus, identifying pathways to decarbonize the aviation sector via liquid low- or net-zero carbon biofuels that are compatible with current aviation infrastructure is crucial. This work investigates the economic feasibility of approaching net-zero carbon sustainable aviation fuel (SAF) from woody biomass via gasification and Fischer-Tropsch synthesis. In doing so, this assessment identifies the economic opportunities and trade-offs of several carbon mitigation strategies coupled with renewable resource interventions required to approach net-zero carbon fuels via biomass. Renewable natural gas and green hydrogen utilization strategies are assessed to examine the impact of fuel yield improvements versus renewable resource cost. Additionally, both carbon capture and sequestration (CCS) and carbon capture and utilization (CCU) are considered in this analysis to help mitigate carbon loss to the atmosphere. Each scenario was assessed on cost, carbon efficiency, energy efficiency, and overall technology-readiness level (TRL). The results of this analysis show that renewable natural gas, green hydrogen, and CCS can be viewed as a low-cost, near-term carbon abatement strategies. However, in the long-term, deployment of more expensive and less technologically mature CCU technologies can make use of point sources of CO2 to boost overall fuel production.

aviation fuel↗
DOE OSTI2022

Decarbonization Scenarios in the United States: Comparing Biofuels Growth in Two Models - GCAM and BSM

Scenarios for deep decarbonization rely on biomass for biofuels, biopower, and bioproducts, often including negative emissions via carbon capture and storage or utilization. Despite the prominence of biomass in many deep decarbonization pathways, critical questions remain about biomass allocation, effects of transportation electrification, the pace of growth, and implications for agriculture and land use. We address these questions through a unique comparison of carbon pricing effects on the growth of biomass utilization and its effects on land use in the United States by comparing results from a multisectoral integrated assessment model, the Global Change Analysis Model [GCAM], with results from a biomass-to-biofuels system dynamics model, the Biomass Scenario Model [BSM]. We contribute to model comparison efforts by analyzing the biomass deployment needed for a scenario consistent with a "Middle of the Road" Shared Socioeconomic Pathway [SSP2] and a representative concentration pathway of 2.6 W/m2. The GCAM scenarios solve for global equilibrium conditions that are consistent with this pathway, including demands for biomass across all economic sectors and representing bioenergy with carbon capture and storage as a technology option. The BSM scenarios assess those biomass and biofuel results for the United States and identify challenges associated with that pace and amount of expansion. In the scenario analysis, we harmonize key factors such as carbon price trajectory, domestic ethanol fuel demand, ethanol blending, and arable land availability, and vary them in both models. In GCAM, we vary the carbon price, transportation electrification, ethanol blending constraints, and arable land availability inputs and the value of the carbon in land; in BSM, in addition to directly inputting certain GCAM results, we vary the maximum rate of biorefinery construction, flexibility of feedstock types across conversion processes, and policy incentives such as tax credits and renewable identification number payments. The selected carbon price trajectory results in a rapid increase in biofuel production in the United States, reaching about 9.4 EJ/year in 2060 in the highest scenario analyzed in GCAM. Results differ between the two models in timing and ultimate quantity of biomass and biofuel production. GCAM biofuel quantities generally exceed BSM amounts because CCS is applied to biofuel pathways in GCAM, and because of differences in capacity expansion and related dynamics of land allocation, biomass production, and price dynamics. These dynamics include rapid biorefinery capacity expansion in high demand cases. To satisfy this biomass demand, GCAM rapidly equilibrates land allocation, but the BSM limits the rate at which this re-allocation can occur. A further contrast with the equilibrium approach in GCAM is that the BSM represents a delay between planting and harvesting woody biomass resources. As a result of these model contrasts, feedstock costs in BSM increase more than in GCAM, and the absence of CCS in the BSM also reduces the relative economic attractiveness of biofuels production. The bottlenecks, lags, and price increases also lead to potential for volatility in feedstock price and land allocation to biomass in the BSM. GCAM has more biomass production than BSM in all scenarios, partly because of the broader, economy-wide coverage of GCAM, in contrast to BSM's exclusive focus on biofuels. In both models, trends like those of biofuels production were observed for biomass production: minimal growth without a carbon price and policy incentives, and increases with a carbon price, particularly with carbon capture and storage, because the inputs assume that biopower and biofuels decrease greenhouse gas emissions. In high policy scenarios, biomass demand is high, and the consequent high biomass prices due to the land re-allocation bottleneck in the BSM limit biofuel production even if the biorefinery capacity is expanded. However, because biomass prices do not increase as much in the low policy scenario, growth is slower and the land-reallocation bottleneck no longer dominates, such that the effect of increased capacity can be seen. Across both the models, a change in assumptions from less to more land availability increases biofuel production in both GCAM and BSM, as the upward pressure on feedstock price and volatility are both reduced.

biofuels↗
DOE OSTI2015

Nominal Device Support for ATCA Shelf Manager

The ATCA standard specifies a mandatory Shelf Manager (ShM) unit which is a key element for the system operation. It includes the Intelligent Platform Management Controller (IPMC) which monitors the system health, retrieves inventory information and controls the Field Replaceable Units (FRUs). These elements enable the intelligent health monitoring, providing high-availability and safety operation, ensuring the correct system operation. For critical systems like ones of tokamak ITER these features are mandatory to support the long pulse operation. The Nominal Device Support (NDS) was designed and developed for the ITER CODAC Core System (CCS), which will be the responsible for plant Instrumentation and Control (I and C), supervising and monitoring on ITER. It generalizes the Enhanced Physics and Industrial Control System (EPICS) device support interface for Data Acquisition (DAQ) and timing devices. However the support for health management features and ATCA ShM are not yet provided. This paper presents the implementation and test of a NDS for the ATCA ShM, using the ITER Fast Plant System Controller (FPSC) prototype environment. This prototype is fully compatible with the ITER CCS and uses the EPICS Channel Access (CA) protocol as the interface with the Plant Operation Network (PON). The implemented solution running in an EPICS Input / Output Controller (IOC) provides Process Variables (PV) to the PON network with the system information. These PVs can be used for control and monitoring by all CA clients, such as EPICS user interface clients and alarm systems. The results are presented, demonstrating the fully integration and the usability of this solution. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Predicting Geologic Behavior in Carbon Storage Projects Using Graph Neural Network

This study was invited to presented at NVIDIA's GTC conference to highlight the potential of Graph Neural Network as a novel and promising methodology for predicting pressure and saturation evolution in carbon storage projects. Carbon capture and storage (CCS) technology plays a pivotal role in mitigating greenhouse gas emissions, facilitating the transition to a low-carbon future. Effective management of subsurface reservoirs is essential to ensure the safe and efficient storage of captured carbon dioxide (CO₂). Accurate predictions of pressure and saturation over time are critical for evaluating the long-term performance and integrity of CCS projects. In recent years, Graph Neural Network (GNN) has emerged as a powerful framework for analyzing complex data in graph-structured domains. This abstract explores the application of GNN to forecast pressure and saturation evolution in carbon storage projects. Traditional numerical simulations of subsurface reservoirs have proven successful in providing pressure and saturation forecasts. However, these simulations involve massive amounts of computational effort and require extensive domain expertise for proper model calibration and validation. Graph Neural Operator offers an alternative approach that harnesses the inherent graph structure of reservoirs, where nodes represent reservoir grid cells and edges represent the geological connectivity between them.

Shih, Chung Yan↗
DOE OSTI2024

Decarbonizing Hydrogen Production: Assessing A Net-Negative Pathway

Hydrogen is gaining prominence as a key factor in the world's transition to a cleaner energy future. The International Energy Agency (IEA)'s Global Hydrogen Review 2023 reports that the number of low-emission hydrogen production projects is increasing rapidly. The potential for growth in new applications such as heavy industry, transportation, and power generation is significant. The IEA urges more decisive action to spur demand for low-emission hydrogen to achieve climate goals. While hydrogen is produced through various industrial methods, each with its own advantages and disadvantages, low-carbon hydrogen is critical for mitigating climate change and is incentivized by the Clean Hydrogen Production Tax Credit (45V). To this end, we have evaluated a commercial technology that can produce low- or negative-carbon hydrogen via ethanol catalytic oxidative reforming. This study assessed life cycle greenhouse gas emissions (carbon intensity or CI) associated with the hydrogen production technology. A total of 24 scenarios were evaluated, encompassing (a) Gen1 versus Gen2 ethanol inputs, (b) carbon capture and sequestration (CCS) of upstream fermentation CO2, and (c) oxygen sourcing via air separation unit (ASU) versus purchased or on-site production of oxygen as a byproduct of hydrogen electrolysis with a proton exchange membrane (PEM). Key findings include that the base case CI for hydrogen production using Gen2 ethanol from corn stover is lower than Gen1 dry mill corn ethanol. The study also points out that the CI for hydrogen production using PEM-O2 is lower than that using ASU-O2, whether the PEM-O2 is produced on-site or off-site (importing). When sourcing oxygen from on-site PEM-O2, the Gen1 and Gen2 ethanol-derived hydrogen exhibit favorable net-negative CI values for all evaluated scenarios, especially if the upstream ethanol CCS is included. As a reference, the 45V regulatory threshold for generating clean hydrogen tax credits is a CI below 0.45 kg CO2e/kg hydrogen.

BIOMASS FUELS,HYDROGEN↗
DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

This is the conference paper accompanying an oral presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗
DOE OSTI2025

Evaluation of Drilling Performance at The Geysers with Machine Learning Methods Using Geologic Data

A recent well, GDC-36, was drilled in The Geysers Geothermal Field served in a Department of Energy-industry to demonstrate improved drilling performance with polycrystalline diamond compact (PDC) bits. Both PDC and roller cone drill bits were used to drill this well. Key challenges encountered during drilling included lost circulation in the mud-drilled section, and bit damage interfacial severity in the deeper, air-drilled section. The objective of this study is to evaluate the drilling performance in relation to the local geological characteristics using machine learning methods. By applying K-clustering to the sonic log data, we were able to identify areas correlated with measured lost circulation. Also, the boundaries defined by clustering of the mineralogical and lithological data from the mud logs correlate well with interfacial severity during drilling. A random forest model was employed to build correlation between drilling data and rock strength. The confined compressive strength (CCS) of the rock in the training of the machine learning model was inferred from the dipole sonic log. The R-squared of the testing data is 0.78, and the RMSE (Root Mean Squared Error) is 0.06. The trained model was used to forecast rock strength for the section where sonic log data are not available. CCS could also be inferred from mud logs provided the relationship between mineralogy and rock strength is established through core testing data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Hydrogeological assessment of CO2 containment assurance and wellbore integrity at a Gulf Coast storage site

Abstract A large-scale carbon capture and storage (CCS) initiative on the Texas Gulf Coast serves as a premier demonstration of the U.S. Department of Energy’s CarbonSAFE program. Targeting deep saline formations, specifically Oligo-Miocene deltaic sequences, the project aims to establish technical and commercial viability for geologic CO2 storage within a major industrial corridor. This study provides a rigorous hydrogeological assessment to support Class VI permitting by quantifying the high degree of containment security. Utilizing a compositional reservoir simulator, we developed a suite of 27 distinct simulation cases to evaluate vertical plume dynamics near both planned injection wells and proximal legacy infrastructure. To ensure numerical accuracy near wellbores, we implemented a refined mesh strategy, determining that a 5.6 ft × 5.6 ft grid refinement offered the optimal balance between computational efficiency and descriptive precision. The modeling framework utilized a systematic sensitivity-based approach to evaluate the mechanical redundancy of the subsurface system by performing a bounding analysis of wellbore interfaces against hypothetical high-permeability microannuli. By systematically isolating competing physical drivers, including permeability, porosity, gas hysteresis, thermal gradients, salinity, and solubility trapping (quantified via Henry’s law with dynamically adjusted coefficients), this work moves beyond binary assessments to establish a nuanced hierarchy of containment factors. The results confirm that primary trapping mechanisms (e.g., gas hysteresis and solubility), combined with the site's unique geomechanical stratigraphy, significantly restrict vertical mobility and reinforce the robust containment security of the reservoir. Baseline results demonstrate substantial vertical separation between the CO2 plume and the upper confining system, ensuring robust containment. Sensitivity analysis reveals that even under highly conservative bounding scenarios—assuming theoretical 10-Darcy pathways at specific wellbore locations—the 2,900-ft thick multi-layered confining zone remains a reliable barrier. In these hypothetical upper-bound cases, peak upward fluxes of CO2 and saltwater after 15 years of injection remain localized and dissipate rapidly within the lower sections of the confining interval, leaving the integrity of the seal uncompromised. Furthermore, the study identifies that while localized wellbore pathways define theoretical upper bounds of vertical migration, the Area of Review (AoR) is primarily sensitive to regional thermal gradients and hysteresis, which can influence the AoR by over 3,000 acres in pessimistic configurations. Also, primary trapping mechanisms, specifically gas hysteresis and solubility, work in tandem with the Gulf Coast’s unique geomechanical stratigraphy to significantly restrict vertical mobility. Ductile, smectite-rich mudstones facilitate natural borehole convergence and the self-healing of potential conduits, creating a natural geomechanical bridge that effectively mitigates migration potential at both current injection points and legacy-well locations. This comprehensive modeling effort demonstrates that the integration of high-resolution wellbore simulations and regional geomechanical observations confirms the long-term storage security of the studied site, providing a physics-based foundation for industrial-scale CCS deployments. This modeling framework establishes a baseline for future research into coupled geomechanical effects, such as time-dependent borehole convergence, to further refine long-term containment projections. Acknowledgements We thank the Gulf Coast Carbon Center (GCCC) at the Bureau of Economic Geology for foundational research support. We appreciate Alex Bump for technical guidance and David Hoffman for model mesh generation. This work used TACC’s Frontera cluster for simulations and CMG Ltd. software licenses provided to UT-Austin. This material is based upon work supported by the Department of Energy under Award Number DE-FE0032338. Disclaimer This material is based upon work supported by the U.S. Department of Energy’s Fossil Energy and Carbon Management Office under the CarbonSAFE program, award Number DE-FE0032338. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government.

58 GEOSCIENCES↗
DOE OSTI2026

Hydrogeological assessment of CO2 containment assurance and wellbore integrity at a Gulf Coast storage site

Abstract A large-scale carbon capture and storage (CCS) initiative on the Texas Gulf Coast serves as a premier demonstration of the U.S. Department of Energy’s CarbonSAFE program. Targeting deep saline formations, specifically Oligo-Miocene deltaic sequences, the project aims to establish technical and commercial viability for geologic CO2 storage within a major industrial corridor. This study provides a rigorous hydrogeological assessment to support Class VI permitting by quantifying the high degree of containment security. Utilizing a compositional reservoir simulator, we developed a suite of 27 distinct simulation cases to evaluate vertical plume dynamics near both planned injection wells and proximal legacy infrastructure. To ensure numerical accuracy near wellbores, we implemented a refined mesh strategy, determining that a 5.6 ft × 5.6 ft grid refinement offered the optimal balance between computational efficiency and descriptive precision. The modeling framework utilized a systematic sensitivity-based approach to evaluate the mechanical redundancy of the subsurface system by performing a bounding analysis of wellbore interfaces against hypothetical high-permeability microannuli. By systematically isolating competing physical drivers, including permeability, porosity, gas hysteresis, thermal gradients, salinity, and solubility trapping (quantified via Henry’s law with dynamically adjusted coefficients), this work moves beyond binary assessments to establish a nuanced hierarchy of containment factors. The results confirm that primary trapping mechanisms (e.g., gas hysteresis and solubility), combined with the site's unique geomechanical stratigraphy, significantly restrict vertical mobility and reinforce the robust containment security of the reservoir. Baseline results demonstrate substantial vertical separation between the CO2 plume and the upper confining system, ensuring robust containment. Sensitivity analysis reveals that even under highly conservative bounding scenarios—assuming theoretical 10-Darcy pathways at specific wellbore locations—the 2,900-ft thick multi-layered confining zone remains a reliable barrier. In these hypothetical upper-bound cases, peak upward fluxes of CO2 and saltwater after 15 years of injection remain localized and dissipate rapidly within the lower sections of the confining interval, leaving the integrity of the seal uncompromised. Furthermore, the study identifies that while localized wellbore pathways define theoretical upper bounds of vertical migration, the Area of Review (AoR) is primarily sensitive to regional thermal gradients and hysteresis, which can influence the AoR by over 3,000 acres in pessimistic configurations. Also, primary trapping mechanisms, specifically gas hysteresis and solubility, work in tandem with the Gulf Coast’s unique geomechanical stratigraphy to significantly restrict vertical mobility. Ductile, smectite-rich mudstones facilitate natural borehole convergence and the self-healing of potential conduits, creating a natural geomechanical bridge that effectively mitigates migration potential at both current injection points and legacy-well locations. This comprehensive modeling effort demonstrates that the integration of high-resolution wellbore simulations and regional geomechanical observations confirms the long-term storage security of the studied site, providing a physics-based foundation for industrial-scale CCS deployments. This modeling framework establishes a baseline for future research into coupled geomechanical effects, such as time-dependent borehole convergence, to further refine long-term containment projections. Acknowledgements We thank the Gulf Coast Carbon Center (GCCC) at the Bureau of Economic Geology for foundational research support. We appreciate Alex Bump for technical guidance and David Hoffman for model mesh generation. This work used TACC’s Frontera cluster for simulations and CMG Ltd. software licenses provided to UT-Austin. This material is based upon work supported by the Department of Energy under Award Number DE-FE0032338. Disclaimer This material is based upon work supported by the U.S. Department of Energy’s Fossil Energy and Carbon Management Office under the CarbonSAFE program, award Number DE-FE0032338. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government.

58 GEOSCIENCES↗
DOE OSTI2021

Common risk segment mapping: Streamlining exploration for carbon storage sites, with application to coastal Texas and Louisiana

Large-scale deployment of Carbon Capture and Storage (CCS) will require a commensurately large number of sites. Efficient screening methods are needed to create investment assurance and focus efforts on the most promising sites. The problem is similar to petroleum exploration, for which there are well-developed (though seldom published) workflows, including Common Risk Segment (CRS) mapping. In brief, the process requires 1) defining the key play elements; 2) identifying candidate geologic intervals for each; 3) creating fact-based maps for those intervals; 4) determining minimum criteria for the success of each element; 5) reinterpreting the fact-based maps in terms of chance of success; and 6) combining the individual maps to form a composite, basin-scale view of prospectivity. We adapt the CRS process to screening for CO 2 storage sites. Critically, we redefine the process in terms of cost of characterization and development, rather than chance of success. For illustration, we apply the process to the example of the Lower Miocene on the Texas and Louisiana Gulf Coast. We show that the predictions are consistent with historic hydrocarbon production volumes and rates. The power of the CRS method is that it creates a systematic approach to geologic evaluation and translates complex, multidimensional analysis into clear, graphical and easily comprehended business inputs. The results highlight sweet spots and identifies critical risks, suggesting a focus for further data collection and analysis. Furthermore, the method developed here can be applied to both surface and subsurface factors anywhere that there is interest in geologic storage of CO 2 .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Fetch-trap Pairs: Exploring definition of carbon storage prospects to increase capacity and flexibility in areas with competing uses

As CCS grows beyond isolated first-mover projects, the question of how to identify potential storage sites (prospects) is key to siting projects amidst the constraints of neighboring storage projects, hydrocarbon fields and other considerations. To date, prospects have been defined either by the extent of a buoyant trap or by the predicted maximum extent of the CO 2 or pressure plume. Both approaches have worked, but they may struggle in places like the Gulf of Mexico where complex geologic structure can invalidate simple assumptions of radial plume spread and buoyant traps are commonly unattractive due to numerous legacy well penetrations. Here, we propose identifying prospective storage sites by fetch area (drainage cell) rather than by buoyant closure alone. Doing so offers 1) greater freedom in siting injectors to avoid surface and subsurface constraints, including legacy wells that tend to cluster on structural high; and 2) a coherent flow regime in which buoyancy drives all injected CO 2 toward a common high. This strategy gives space to dissipate injection pressure and minimize the number of legacy wells needing review. It may also offer 1) better injectivity by bringing synclines and associated channel axes into play; and 2) potentially improved capacity by tapping a larger rock volume and taking advantage of migration losses.

58 GEOSCIENCES↗
DOE OSTI2023

Minimizing exposure to legacy wells and avoiding conflict between storage projects: Exploring area of review as a screening tool

Elevated pressure from large-volume injection is a key driver of risk and project cost. If transmissive features (e.g., non-isolating wells or fracture systems) are present, increased injection-zone pressure can drive fluids from depth toward protected freshwater resources. In US Carbon Capture and Storage (CCS) law, the area at risk is known as the Area of Review (AoR). The size and number of potentially transmissive features to be evaluated and possibly remediated or managed is a function of the size and location of the AoR. The size of the AoR depends on several variables, including properties of the injection zone, properties of protected resources, and injection rate and duration. Evaluation of the intersection of these variables across a portfolio of sites highlights the injection zone depth and boundary conditions as top-level controls. Deep injection, use of multiple stacked injection zones, reduced injection rate and choice of injection well location can all be used to minimize AoR and the number of potentially transmissive features within it. Here, we introduce the concept of pressure space (defined as connected pore volume times pressure) as the key subsurface commodity for CO 2 storage and we suggest that it forms a more robust basis for leasing and regulation than pore space alone.

58 GEOSCIENCES↗