Carbon capture, utilization, and storage (CCUS) in geological formations play a key role in mitigating anthropogenic CO2 emissions and achieving the aggressive goal of net-zero greenhouse gas emissions. Risk and uncertainty assessment is crucial for ensuring the safety and reliability of geologic carbon storage (GCS) by evaluating CO2 migration in subsurface, forecasting potential leakage and induced seismicity risks, and optimizing operational and monitoring plans. In this review, the use and progress of risk assessment for GCS over the last few decades are examined. We use the Southwest Regional Partnership on Carbon Sequestration (SWP), which is one of the seven regional partnerships supported by the United States Department of Energy (U.S. DOE), as an example of large-scale CCUS projects in North America. Additionally, future trends and requirements for risk assessment in GCS are discussed. The information provided in this review can help readers understand the significance of risk and uncertainty assessment and apply it effectively in large-scale GCS projects.

The Farnsworth Unit in northern Texas is a field site for studying geologic carbon storage during enhanced oil recovery (EOR) using CO2. Microseismic monitoring is essential for risk assessment by detecting fluid leakage and fractures. We analyzed borehole microseismic data acquired during CO2 injection and migration, including data denoising, event detection, event location, magnitude estimation, moment tensor inversion, and stress field inversion. We detected and located two shallow clusters, which occurred during increasing injection pressure. The two shallow clusters were also featured by large b values and tensile cracking moment tensors that are obtained based on a newly developed moment tensor inversion method using single-borehole data. The inverted stress fields at the two clusters showed large deviations from the regional stress field. The results provide evidence for microseismic responses to CO2/fluid injection and migration.

Attaining reliable forecasts in the petroleum industry is a constant challenge, prompting ongoing research into models that empower engineers to make informed decisions. The conventional approach suffers from high uncertainties with complex input parameters resulting in convergence issues and high computational costs. The emergence of timeseries machine-learning models has shown to leverage trends and pattern recognition to achieve tractable, robust and cost-effective solutions. While previous research primarily focused on predicting existing production trends, this study uses timeseries analysis to forecast oil recovery in a CO2-EOR reservoir, focusing on an inverted five-spot pattern. Dynamic data encompassing pressures, WAG cycles, and injection volumes undergo preprocessing and chronological division to facilitate training and testing of an LSTM model. The analysis of field history calibration through the loss iteration of the training dataset shows promising results reflected in low mean-squared-error of 6.65. Model validation, including the test and blind datasets, demonstrates an approximate R-squared value of 0.88. The forecasted results unveil mounting uncertainty over time, and sensitivity analysis shows the significance of WAG cycle adjustments. This study introduces an economical oil recovery forecasting approach, devoid of complex physical models, offering a versatile framework applicable across disciplines, especially for scenarios influenced by past decisions.

About one million tons of CO2 have been injected into the Farnsworth unit to date. The target reservoir for CO2 injection is the Morrow B Sandstone, which is primarily made of quartz with lesser amounts of albite, calcite, chlorite, and clay minerals. The impact of CO2 injection on the mineralogy, porosity, and pore water composition of the Morrow B Sandstone is a major concern. Although numerical modeling studies suggest that porosity changes will be minimal, significant alterations to mineralogy and pore water composition are expected. Given the implications for CO2 storage effectiveness and risk assessment, it is crucial to verify the accuracy of theoretical model predictions through laboratory experiments. To this end, batch reaction experiments were conducted to model conditions near an injection well in the Morrow B Sandstone and at locations further away, where the CO2 has been diluted by formation water. The laboratory experiments involved submerging thin sections of both coarse- and fine-grained facies of the Morrow B Sandstone in formation water samples with varying levels of CO2. The experiments were conducted at the reservoir temperature of 75 °C. Two experimental runs were conducted, one lasting for 61 days and the other for 72 days. The initial fluid composition used in the second run was the same as in the first. The mineralogy changes in the thin sections were analyzed using SEM and the Tescan Integrated Mineral Analyzer (TIMA), while changes in the composition of the formation water were determined using ICP-AES. During each experiment, a thin layer of white fine-grained particles consisting mainly of dolomite and silica formed on the surface of the thin sections, leading to significant reductions in Ca, Mg, and Sr in the formation water. This outcome is consistent with numerical model predictions that dolomite would be the primary mineral that would react with injected CO2 and that silica would be oversaturated in the formation water. Changes in mineral abundance in the thin sections themselves were much less systematic than in the theoretical modeling experiments, perhaps reflecting heterogeneities in the mineral grain size surface area to volume ratios and mineral distributions in the thin sections not considered in the numerical models.

During the Development Phase of the U.S. Southwest Regional Partnership on Carbon Sequestration, supercritical CO2 was continuously injected into the deep oil-bearing Morrow B formation of the Farnsworth Unit in Texas for Enhanced Oil Recovery (EOR). The project injected approximately 94 kilotons of CO2 to study geologic carbon storage during CO2-EOR. A three-dimensional (3D) surface seismic dataset was acquired in 2013 to characterize the subsurface structures of the Farnsworth site. Following this data acquisition, the baseline and three time-lapse three-dimensional three-component (3D-3C) vertical seismic profiling (VSP) data were acquired at a narrower surface area surrounding the CO2 injection and oil/gas production wells between 2014 and 2017 for monitoring CO2 injection and migration. With these VSP datasets, we inverted for subsurface velocity models to quantitatively monitor the CO2 plume within the Morrow B formation. We first built 1D initial P-wave (Vp) and S-wave (Vs) velocity models by upscaling the sonic logs. We improved the deep region of the Vp and Vs models by incorporating the deep part of a migration velocity model derived from the 3D surface seismic data. We improved the shallow region of 3D Vp and Vs models using 3D traveltime tomography of first arrivals of VSP downgoing waves. We further improved the 3D baseline velocity models using elastic-waveform inversion (EWI) of the 3D baseline VSP upgoing data. Our advanced EWI method employs alternative tomographic and conventional gradients and total-variation-based regularization to ensure the high-fidelity updates of the 3D baseline Vp and Vs models. We then sequentially applied our 3D EWI method to the three time-lapse datasets to invert for spatiotemporal changes of Vp and Vs in the reservoir. Our inversion results reveal the volumetric changes of the time-lapse Vp and Vs models and show the evolution of the CO2 plume from the CO2 injection well to the oil/gas production wells.

Leakage from geologic CO2 sequestration (GCS) sites to overlying shallow drinking water aquifers is a tangible risk. A primary purpose of this study is to assess the potential impacts of CO2 leakage into a fresh-water aquifer with associated CO2-water-sediment interactions. The study site is the Ogallala aquifer overlying an active demonstration-scale GCS site in north Texas, USA. Using the results of combined batch experiments and reactive transport simulations, we discuss the effects of salinity on potential trace metal release and the potential for groundwater quality recovery after leakage ceases. RESULTS: suggest that trace metals are released from sediment due to impure carbonate mineral dissolution and cation exchange with exposure to aqueous CO2. Concentrations of Mn, Zn and Sr might exceed the U.S. Environmental Protection Agency's (EPA) limits. After CO2 leakage stops, most cation concentrations decrease to levels observed before leakage quickly, suggesting that water quality may not be a long-term concern. However, saline water that co-leaks with CO2 may increase salinity of a shallow aquifer and induce more trace metals release from the sediment. In most cases, pH is sensitive to even small increases of CO2, suggesting that pH may be a sufficiently sensitive parameter for detecting CO2 leakage.

Over the years, naturally occurring CO2 has been used in many enhanced oil recovery (EOR) projects in the United States [...]

As part of the project funded under the Carbon Utilization and Storage Partnership (CUSP) of the Western United States, this paper demonstrates a workflow including site characterization and numerical simulation efforts of proposing a Monitoring, Reporting, and Verification (MRV) plan to the U.S. Environmental Protection Agency (EPA) for approval according to 40 CFR 98.440 (c)(1), Subpart RR of the Greenhouse Gas Reporting Program (GHGRP) to qualify for the tax credit in section 45Q of the federal Internal Revenue Services (IRS) Code. In this project, the injectors and treated acid gas (TAG) plant are located at the northern margin of the Delaware Basin, a highly productive hydrocarbon basin in southeastern New Mexico. The target injection zones are the Permian-aged Cherry Canyon Formation for the acid gas injection (AGI) #1 well and Siluro-Devonian formations for the AGI #2 well, storage zones above and beneath active hydrocarbon pay zones respectively. The storage zones and caprocks are characterized through well log examinations, formation fluid chemistry evaluation, faults identification and interpretation. Reservoir models were constructed and simulation performed to predict the extent of the TAG plume after 30 years of injection with 5 years of post-injection site care monitoring. The reservoir mapping and cross sections interpreted from well logs indicate that the area around AGI #1 does not contain visible faulting or offsets that might influence fluid migration, suggesting that injected fluid would spread radially from the point of injection with a small elliptical component to the south. In the Siluro-Devonian formation, where AGI #2 is planned to be completed. The induced-seismicity risk assessment shows that the operation of the proposed injection combined with the historic volume contributions of the regional saltwater disposal (SWD) wells is not anticipated to contribute significantly to injection-induced fault slip. This result demonstrates that acid gas can be injected as proposed while maintaining the minimal risk of induced seismicity. The water sample collected from a nearby well indicates that the formation waters are highly saline (180,000 ppm NaCl) and compatible with the proposed injection. The reservoir simulation results indicate that the TAG plume is predicted to extend a maximum of 1.2 km from the injector wellbore when the identified faults are treated as non-transmissive and 0.90 km when they are treated as transmissive. The pressure profiles demonstrate the strong potential for safe injection into both target formations. In December 2021, the United States Environmental Protection Agency (EPA) approved the Monitoring, Reporting, and Verification (MRV) plan, permitting Lucid Energy to sequester acid gas from its Red Hills gas processing complex in Lea County, New Mexico. This paper provides the industry with a critical roadmap for converting existing injectors into CO2 or TAG sequestration wells that may qualify for 45Q tax certification to comply with the current administrative regulations. As part of the project funded by Carbon Utilization and Storage Partnership (CUSP) of the Western United States, published data from this project is invaluable.

The purpose of this study was to quantify changes to underground sources of drinking water (USDW) quality in response to potential CO2 leakage from geologic CO2 sequestration (GCS) reservoirs. We developed a framework of combined laboratory experiments and reactive transport simulations and used this framework to evaluate the Ogallala aquifer overlying the Farnsworth Unit (FWU), an active GCS site, as a case study. Using chemical reaction parameters obtained from laboratory experiments and numerical simulations, site-specific mechanisms of CO2-water-sediment interactions at the USDW aquifer were interpreted. Long-term risks of potential CO2 leakage were then evaluated with field-scale numerical models using the regional hydrogeological characteristics and reaction parameters obtained from our experiments and simulations. Results suggest that carbonate mineral impurity and cation exchange are key mechanisms for interactions between CO2 and the aquifer sediment. Additionally, for a large leakage rate of 0.1 % injection from one leaky well, the leakage plume might impact an area of 300 m in diameter and significantly affect the local water quality by changing pH and cation concentrations (e.g., Zn, Ba and Sr). After leakage ceases, the zone of impacted fluids would not migrate significantly in subsequent decades due to a low regional groundwater flowrate (for this case study). The relatively small area of impact might not be detected in a monitoring well given the broader spacing in a typical field scenario. Effective early leakage detection may require additional tools, e.g., borehole CO2 movement, four-dimensional seismicity, CO2 soil flux, samples from deeper aquifers, etc., to ensure effective leakage detection and long-term safety of GCS projects.

This paper presents probabilistic methods to estimate the quantity of carbon dioxide (CO2) that can be stored in a mature oil reservoir and analyzes the uncertainties associated with the estimation. This work uses data from the Farnsworth Field Unit (FWU), Ochiltree County, Texas, which is currently undergoing a tertiary recovery process. The input parameters are determined from seismic, core, and fluid analyses. The results of the estimation of the CO2 storage capacity of the reservoir are presented with both expectation curve and log probability plot. The expectation curve provides a range of possible outcomes such as the P90, P50, and P10. The deterministic value is calculated as the statistical mean of the storage capacity. The coefficient of variation and the uncertainty index, P10/P90, is used to analyze the overall uncertainty of the estimations. A relative impact plot is developed to analyze the sensitivity of the input parameters towards the total uncertainty and compared with Monte Carlo. In comparison to the Monte Carlo method, the results are practically the same. The probabilistic technique presented in this paper can be applied in different geological settings as well as other engineering applications.

We present the current status of time-lapse seismic integration at the Farnsworth (FWU) CO2 WAG (water-alternating-gas) EOR (Enhanced Oil Recovery) project at Ochiltree County, northwest Texas. As a potential carbon sequestration mechanism, CO2 WAG projects will be subject to some degree of monitoring and verification, either as a regulatory requirement or to qualify for economic incentives. In order to evaluate the viability of time-lapse seismic as a monitoring method the Southwest Partnership (SWP) has conducted time-lapse seismic monitoring at FWU using the 3D Vertical Seismic Profiling (VSP) method. The efficacy of seismic time-lapse depends on a number of key factors, which vary widely from one application to another. Most important among these are the thermophysical properties of the original fluid in place and the displacing fluid, followed by the petrophysical properties of the rock matrix, which together determine the effective elastic properties of the rock fluid system. We present systematic analysis of fluid thermodynamics and resulting thermophysical properties, petrophysics and rock frame elastic properties, and elastic property modeling through fluid substitution using data collected at FWU. These analyses will be framed in realistic scenarios presented by the FWU CO2 WAG development. The resulting fluid/rock physics models will be applied to output from the calibrated FWU compositional reservoir simulation model to forward model the time-lapse seismic response. Modeled results are compared with field time-lapse seismic measurements and strategies for numerical model feedback/update are discussed. While mechanical effects are neglected in the work presented here, complementary parallel studies are underway in which laboratory measurements are introduced to introduce stress dependence of matrix elastic moduli.

In 2019, The United States Department of Energy created four new regional carbon storage partnerships following the success of the previous regional partnership program established in 2003. The new partnerships are designed to accelerate development of commercial storage projects in the United States. The Carbon Utilization and Storage Partnership of the Western United States (CUSP) was formed as an outgrowth of three of the former partnerships: The Southwest Regional Partnership on Carbon Sequestration; the WestCArb partnership; and the Big Sky Partnership (see map). The CUSP primary objective is to catalog, analyze and rank CCUS options for parts or all of 13 states that make up the contiguous western USA. The multi-state CUSP team and project coordinates the capabilities, experience, data collection methods and advanced modelling tools developed and refined through several decades of efforts via many previous NETL-sponsored projects. The CUSP team seeks to accelerate CCUS technology development and deployment in the Western U.S., with a Partnership that consists of 13 universities, seven geological surveys, three research institutes and three national laboratories. Goals of the CUSP project include assembly of existing CCUS data into a uniform database, and increased data collection and analysis of new data not yet present in EDX, NATCARB or other databases. Additionally, the CUSP seeks improvement of modeling tools used for risk prediction and economic scenario analysis, and identification of major technical challenges and development of CCUS deployment readiness indices. One primary deliverable of the CUSP project maps, interactive software and data products that delineate not only regions and specific targets that have the best prospects for commercially-viable CCUS, but also highlight technical challenges and their effects on CCUS development. Other products include analysis tools and large data sets tailored for machine learning efforts via the new DOE initiative on machine learning.These goals are being accomplished by updating existing data (for example, CO2 storage data collected during the creation of the National Carbon Atlas, EPA CO2 source data, and pipeline and infrastructure data), augmenting and refining that data where gaps are identified, and feeding the data into various analytical and optimization models to create a series of readiness indices for the Western Region of the United States. In addition to reducing geological characterization uncertainty, particularly for stacked storage reservoirs (saline+EOR), the project is incorporating a variety of soft data into models that will help identify the best prospects for commercially-viable CCUS and help quantify potential economic impact. This effort provides targeted regions with the most promising combination of geology, geography and infrastructure and industrial sectors for short term, mid-term, and long-term CCUS projects, and identifies improvements necessary for maximizing success and assess scenarios that can swiftly and cost-effectively graduate potential projects to short-term status. Via SimCCS and additional desktop-based software tools, dynamic readiness mapping are developed and verified, modified as needed, and distributed. Use of these tools are supported by educational workshops and online training materials. Finally, the CUSP Partnership is participating in a variety of technology transfer programs to help facilitate regional efforts to guide and develop policies and permitting mechanisms to further CCUS in this region.The benefits of the CUSP project include improving the quality and interoperability of existing data, incorporation of more extensive, and detailed, technical as well as soft data into very large data sets for Machine Learning efforts, and using local and regional expertise to continue and improve technology transfer and stimulate stakeholder interest and knowledge of CCUS. Progress to date for major objectives will be presented, including preliminary heat maps of CCS/CCUS potential in the western United States.

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

Farnsworth Field Unit (FWU), a mature oilfield currently undergoing CO2-enhanced oil recovery (EOR) in the northeastern Texas panhandle, is the study area for an extensive project undertaken by the Southwest Regional Partnership on Carbon Sequestration (SWP). SWP is characterizing the field and monitoring and modeling injection and fluid flow processes with the intent of verifying storage of CO2 in a timeframe of 100–1000 years. Collection of a large set of data including logs, core, and 3D geophysical data has allowed us to build a detailed reservoir model that is well-grounded in observations from the field. This paper presents a geological description of the rocks comprising the reservoir that is a target for both oil production and CO2 storage, as well as the overlying units that make up the primary and secondary seals. Core descriptions and petrographic analyses were used to determine depositional setting, general lithofacies, and a diagenetic sequence for reservoir and caprock at FWU. The reservoir is in the Pennsylvanian-aged Morrow B sandstone, an incised valley fluvial deposit that is encased within marine shales. The Morrow B exhibits several lithofacies with distinct appearance as well as petrophysical characteristics. The lithofacies are typical of incised valley fluvial sequences and vary from a relatively coarse conglomerate base to an upper fine sandstone that grades into the overlying marine-dominated shales and mudstone/limestone cyclical sequences of the Thirteen Finger limestone. Observations ranging from field scale (seismic surveys, well logs) to microscopic (mercury porosimetry, petrographic microscopy, microprobe and isotope data) provide a rich set of data on which we have built our geological and reservoir models.

Leakage pathways through caprock lithologies for underground storage of CO2 and/or enhanced oil recovery (EOR) include intrusion into nano-pore mudstones, flow within fractures and faults, and larger-scale sedimentary heterogeneity (e.g., stacked channel deposits). To assess multiscale sealing integrity of the caprock system that overlies the Morrow B sandstone reservoir, Farnsworth Unit (FWU), Texas, USA, we combine pore-to-core observations, laboratory testing, well logging results, and noble gas analysis. A cluster analysis combining gamma ray, compressional slowness, and other logs was combined with caliper responses and triaxial rock mechanics testing to define eleven lithologic classes across the upper Morrow shale and Thirteen Finger limestone caprock units, with estimations of dynamic elastic moduli and fracture breakdown pressures (minimum horizontal stress gradients) for each class. Mercury porosimetry determinations of CO2 column heights in sealing formations yield values exceeding reservoir height. Noble gas profiles provide a “geologic time-integrated” assessment of fluid flow across the reservoir-caprock system, with Morrow B reservoir measurements consistent with decades-long EOR water-flooding, and upper Morrow shale and lower Thirteen Finger limestone values being consistent with long-term geohydrologic isolation. Together, these data suggest an excellent sealing capacity for the FWU and provide limits for injection pressure increases accompanying carbon storage activities.

This paper presents a machine learning assisted computational workflow to optimize a CO2-WAG project considering both hydrocarbon recovery and CO2 sequestration efficacies. A compositional field-scaled numerical simulation model is structured to investigate the fluid flow dynamics of an on-going CO2-EOR project in the Farnsworth Unit (Texas, US). Artificial-neural-network (ANN) based proxy models are trained to predict time-series project responses including hydrocarbon production, CO2 storage and reservoir pressure data. The outputs of the proxy model not only serve for evaluating the objective function but also provide significant physical and economic constraints to the optimization processes. In this work, the objective function considers both the oil recovery and CO2 sequestration volume. Moreover, the project net present values (NPV) and reservoir pressure are employed to screen the optimum solutions. The proposed optimization workflow couples the Particle Swarm Optimization (PSO) algorithm and the ANN proxies to maximize the prescribed objective function. The results of this work indicate that the presented workflow is a more robust approach to co-optimize the CO2-EOR projects. Results show that the optimized case can store about 94% of the purchased CO2 within Farnsworth Unit. Comparing to the baseline case, the CO2 storage amount of the found optimal case increases by 21.69%, and the oil production improves 8.74%. More importantly, the improvements in CO2 storage and hydrocarbon recovery lead to 8.74% greater project NPV and 19.79% higher overall objective function value, which confirms the success of the developed co-optimization approach for CO2 sequestration and oil recovery. The lessons and experiences earned from this work provides significant insights into the decision-making process of similar CO2-EOR cases.

This paper presents an optimization methodology on field-scale numerical compositional simulations of CO2 storage and production performance in the Pennsylvanian Upper Morrow sandstone reservoir in the Farnsworth Unit (FWU), Ochiltree County, Texas. This work develops an improved framework that combines hybridized machine learning algorithms for reduced order modeling and optimization techniques to co-optimize field performance and CO2 storage. The model's framework incorporates geological, geophysical, and engineering data. We calibrated the model with the performance history of an active CO2 flood data to attain a successful history matched model. Uncertain parameters such as reservoir rock properties and relative permeability exponents were adjusted to incorporate potential changes in wettability in our history matched model. To optimize the objective function which incorporates parameters such as oil recovery factor, CO2 storage and net present value, a proxy model was generated with hybridized multi-layer and radial basis function (RBF) Neural Network methods. To obtain a reliable and robust proxy, the proxy underwent a series of training and calibration runs, an iterative process, until the proxy model reached the specified validation criteria. Once an accepted proxy was realized, hybrid evolutionary and machine learning optimization algorithms were utilized to attain an optimum solution for pre-defined objective function. The uncertain variables and/or control variables used for the optimization study included, gas oil ratio, water alternating gas (WAG) cycle, production rates, bottom hole pressure of producers and injectors. CO2 purchased volume, and recycled gas volume in addition to placement of new infill wells were also considered in the modelling process. The results from the sensitivity analysis reflect impacts of the control variables on the optimum results. The predictive study suggests that it is possible to develop a robust machine learning optimization algorithm that is reliable for optimizing a developmental strategy to maximize both oil production and storage of CO2 in aqueous-gaseous-mineral phases within the FWU.

This poster presents field-scale numerical compositional simulations of CO2 storage mechanisms in the Morrow B sandstone of the Farnsworth Unit (FWU) located in Ochiltree County, Texas. The study examines structural-stratigraphic, residual, solubility and mineral trapping mechanisms. The reactive transport modeling incorporated evaluates the field’s potential for long-term CO2 sequestration and predicts the CO2 injection effects on the pore fluid composition, mineralogy, porosity and permeability. The dynamic CO2 sequestration simulation model was built from an upscaled geocellar model for the Morrow B. This model incorporated geological, geophysical, and engineering data including well logs, core, 3D surface seismic and fluid analysis. We calibrated the model with historical CO2-WAG miscible flood data and used it to evaluate the feasibility and mechanisms for CO2 sequestration. We used the maximum residual phase saturations to estimate the effect of gas trapped due to hysteresis. In addition, gas solubility in the aqueous phase was modelled as function of pressure, temperature and salinity. Lastly, the coupled geochemical reactions, i.e., the characteristic intra-aqueous and mineral dissolution/precipitation reactions were assimilated numerically as chemical equilibrium and rate-dependent reactions respectively. Additional scenarios that involve shut-in of wells were performed and the reservoir monitored for over 1000 years to understand possible mineralization. Changes in permeability as a function of changes in porosity caused by mineral precipitation/dissolution were calibrated to the laboratory chemo-mechanical responses. The study validates the effects of Morrow B petrophysical properties on CO2 storage potential within the FWU. Study results shows: EOR at the tertiary stage of field operations, total amount of CO2 stored in aqueous-gaseous-mineral phases, evolution and dissolution/precipitation of the principal accessory minerals and the time scale over which mineral sequestration took place in the FWU. This study relates the important physics and mechanisms for CO2 storage in the FWU and illustrates the use of the coupled reactive flow. The study serves as a is benchmark for future field-scale reactive transport CO2-EOR projects in similar fields throughout the world.