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The objectives of this study were (1) to assess the fate and impact of CO2 injected into the Morrow B Sandstone in the Farnsworth Unit (FWU) through numerical non-isothermal reactive transport modeling, and (2) to compare the performance of three major reactive solute transport simulators, TOUGHREACT, STOMP-EOR, and GEM, under the same input conditions. The models were based on a quarter of a five-spot well pattern where CO2 was injected on a water-alternating-gas schedule for the first 25 years of the 1000 year simulation. The reservoir pore fluid consisted of water with or without petroleum. The results of the models have numerous broad similarities, such as the pattern of reservoir cooling caused by the injected fluids, a large initial pH drop followed by gradual pH neutralization, the long-term persistence of an immiscible CO2 gas phase, the continuous dissolution of calcite, very small decreases in porosity, and the increasing importance over time of carbonate mineral CO2 sequestration. The models differed in their predicted fluid pressure evolutions; amounts of mineral precipitation and dissolution; and distribution of CO2 among immiscible gas, petroleum, formation water, and carbonate minerals. The results of the study show the usefulness of numerical simulations in identifying broad patterns of behavior associated with CO2 injection, but also point to significant uncertainties in the numerical values of many model output parameters.

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This paper presents field-scale numerical simulations of CO2 injection activities in the Pennsylvanian Upper Morrow sandstone reservoir, usually termed the Morrow B sandstone, in the Farnsworth Unit (FWU) of Ochiltree County, Texas. The CO2 sequestration mechanisms examined in the study include structural-stratigraphic, residual, solubility and mineral trapping. The reactive transport modelling incorporated in the study evaluates the field's potential for long-term CO2 sequestration and predicts the CO2 injection effects on the Morrow B pore fluid composition, mineralogy, porosity, and permeability. The dynamic CO2 sequestration model was built from an upscaled geocellular 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 active CO2-WAG miscible flood data by adjusting control parameters such as reservoir rock properties and Corey exponents to incorporate potential changes in wettability. The history-matched model was then used 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. The coupled approach which involves the aqueous phase solubility and geochemical reactions were modelled prior to import into the compositional simulation model. The viscosities of the liquid-vapor phases were modeled based on the Jossi-Stiel-Thodos Correlation. This correlation depended on the mixture density calculated by the equation of state. The gas solubility coefficients for the aqueous phase were estimated using Henry's law for various components as function of pressure, temperature, and salinity. The characteristic intra-aqueous and mineral dissolution/precipitation reactions were assimilated numerically as chemical equilibrium and rate-dependent reactions respectively. Multiple scenarios were performed to evaluate the effects and potentials of the CO2 sequestrated within the Morrow formation. Additional scenarios that involve shut-in of wells were performed and the reservoir monitored for over 150 years to understand possible dissolution/precipitation of minerals. Changes in permeability as a function of changes in porosity caused by mineral precipitation/dissolution were calibrated to the laboratory chemo-mechanical responses. This confirms the CO2 injection in the morrow B will alter petrophysical properties, such as permeability and porosity in short-term due to the dissolution of calcite. However, further investigation for the long-term effects needs to be conducted. Moreover, the following significant observations are extracted from the result of this study: oil recovery, total volume of CO2 due to multiple trapping mechanisms, effect of salinity, the timescale-view of the dissolution/precipitation evolution in the Morrow B sandstone. Experiences gained from this study offers valuable visions regarding physiochemical storage induced by the CO2 injection activities and may serve as a benchmark case for future CO2-EOR projects when reactive transportations are considered.

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.