This paper presents an integrated numerical framework to co-optimize FOR and CO2 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 CO2 storage. A multi-objective optimization model was defined for maximizing both oil recovery and CO2 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 CO2 storage of 74%. The results showed an improved approach for optimizing oil recovery and CO2 storage in the FWU. The optimization model predicted that about 94% of CO2 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 CO2 storage. Published by Elsevier Ltd.