Using CO2 in enhanced oil recovery (CO2-EOR) is a promising technology for emissions management because CO2-EOR can dramatically reduce sequestration costs in the absence of emissions policies that include incentives for carbon capture and storage. This study develops a multiscale statistical framework to perform CO2 accounting and risk analysis in an EOR environment at the Farnsworth Unit (FWU), Texas. A set of geostatistical-based Monte Carlo simulations of CO2-oil/gas-water flow and transport in the Morrow formation are conducted for global sensitivity and statistical analysis of the major risk metrics: CO2/water injection/production rates, cumulative net CO2 storage, cumulative oil/gas productions, and CO2 breakthrough time. The median and confidence intervals are estimated for quantifying uncertainty ranges of the risk metrics. A response-surface-based economic model has been derived to calculate the CO2-EOR profitability for the FWU site with a current oil price, which suggests that approximately 31% of the 1000 realizations can be profitable. If government carbon-tax credits are available, or the oil price goes up or CO2 capture and operating expenses reduce, more realizations would be profitable. The results from this study provide valuable insights for understanding CO2 storage potential and the corresponding environmental and economic risks of commercial-scale CO2-sequestration in depleted reservoirs.

This article presents numerical simulations of CO2 storage mechanisms in the Pennsylvanian Upper Morrow sandstone reservoir, locally termed the Morrow B sandstone in the Farnsworth Unit (FWU) of Ochiltree County, Texas. The CO2 storage mechanisms considered in the study under a CO2 enhanced oil recovery (EOR) mode include structural-stratigraphic trapping, CO2 dissolution in formation water and oil, and residual trapping. The reservoir simulation model was constructed on the basis of field geophysical, geological, and engineering data such as three-dimensional surface seismic data, well logs, and fluid analysis. A representative fluid sampled from the reservoir was analyzed and used to tune the equation of state. A thermodynamic minimum miscible pressure was subsequently computed and compared to the experimental outcome. A history-matched model was constructed and used as a baseline to determine the effects of different hypothetical injection strategies (that consider CO2 purchase, gas recycling, and infill drilling), water-alternating-gas (WAG) schemes, and variable salinity on CO2 storage. The simulation results showed that a significant amount of stored CO2 was dissolved in residual oil, contributing to enhanced oil recovery from the tertiary stage of the field operations. Supercritical-phase CO2 mass within the reservoir compared to CO2 dissolved in formation water was found to be dependent on the CO2 injection strategy. The residual trapping contribution was significant when hysteresis was considered. Pressure, volume of reservoir fluid present, caprock integrity, and optimized WAG injection strategies were significant parameters determining the long-term CO2 storage capacity within the FWU. Caprock integrity analyses showed that sealing units have excellent storage capacity with the potential to support column heights of up to 10000 ft. This work shows an improved strategy of maximizing CO2 storage within a depleted oil reservoir. The results from this study show that pressure changes within the reservoir should be continuously monitored to enhance CO2 storage. This study serves as a benchmark for future CO2-EOR projects in the Anadarko basin or geologically similar basins throughout the world.

This paper presents a field scale reservoir characterization for a late Pennsylvanian clastic reservoir at the Farnsworth Unit (FWU), located in the northeast Texas Panhandle on the northwest shelf of the Anadarko basin. The characterization is undertaken as part of a Phase III project conducted by the Southwest Regional Partnership on Carbon Sequestration (SWP). The target unit is the upper most Morrow sandstone bed (Morrow B Sand). Extensive data acquired from FWU was used to improve previously constructed static and dynamic models. The Morrow B reservoir was deposited as fluvial low-stand to transgressive clastic fill within an incised valley. It is predominantly, subarkosic, brown to grey, upper medium to very coarse sands and fine gravels with sub-angular, to sub-rounded poorly sorted grains either planar to massively bedded. It was shown that primary depositional fabrics have less effect than post depositional diagenetic features do on reservoir performance, although subtle variations in deposition may have had some effect on later diagenetic pathways. Three new wells were drilled for the purpose of field infilling and characterization. Cores and advanced wire-line logs from these wells were analyzed for stratigraphic context, sedimentological character and depositional setting in order to better predict porosity and permeability trends within the reservoir. Structural modeling was conducted through the integration of depth-converted 3D seismic data with well log data to create the framework stratigraphic intervals. This information, together with additional core, UBI image logs and an improved hydraulic flow unit methodology (HFU) was used to characterize and subsequently create a fine scale lithofacies based geological model of the field. Core and log analysis allowed subdivision of the target interval into Hydraulic Flow Units (HFUs). The HFU approach enhanced core analysis and was used to elucidate porosity–permeability correlations. This methodology proved to be an exceptional approach to assigning permeability as a function of porosity during petrophysical modeling. The integrated approach of combining seismic attributes with core calibrated facies and the HFU methodology was able to better constrain uncertainty within inter-well spacing and accurately quantify reservoir heterogeneity within FWU. The approach illustrated in this study presents an improved methodology in characterizing heterogeneous and complex reservoirs that can be applied to reservoirs with similar geological features.

This paper presents an integrated numerical framework to co-optimize EOR and CO 2 storage performance under uncertainty in the Farnsworth Unit (FWU) oil field in Ochiltree County, Texas. The framework includes a field-scale compositional reservoir multiphase flow model, an uncertainty quantification model and a neural network optimization process. The reservoir flow model has been constructed based on the field geophysical, geological, and engineering data. Equation of state parameters were tuned to achieve field measured fluid properties and subsequently used to predict the minimum miscible pressure (MMP). A history match of primary and secondary recovery processes was conducted to estimate the reservoir and multiphase flow parameters as the base case for analyzing the effect of recycling produced gas, infill drilling and water alternating gas (WAG) cycles on oil recovery and CO 2 storage. A multi-objective optimization model was defined for maximizing both oil recovery and CO 2 storage. The uncertainty quantification model comprising the Latin Hypercube sampling, Monte Carlo simulation, and sensitivity analysis, was used to study the effects of uncertain variables on the defined objective functions. Uncertain variables include bottom hole injection pressure, WAG cycle, injection and production group rates, and gas-oil ratio. The most significant variables were chosen as control variables to be used for the optimization process. A neural network optimization algorithm was utilized to optimize the objective function both with and without geological uncertainty. The vertical permeability anisotropy (Kv/Kh) was selected as one of the uncertain parameters in the optimization process. The simulation results were compared to a scenario baseline case that predicted CO 2 storage of 74%. The results showed an improved approach for optimizing oil recovery and CO 2 storage in the FWU. The optimization model predicted that about 94% of CO 2 would be stored and most importantly, that this increased storage could result in about 25% of incremental oil recovery. The sensitivity analysis reduced the number of control variables to decrease computational time. A risk aversion factor was used to represent results at various confidence levels to assist management in the decision-making process. The defined objective functions were shown to be a robust approach to co-optimize oil recovery and CO 2 storage.

A review of the state-of-the-art applications of machine learning for CO2 capture, transport, storage, and utilisation.