Capacity assessment and co-optimization of CO2 storage and enhanced oil recovery in residual oil zones

Abstract

Residual oil zones (ROZs) are increasingly being commercially exploited using CO2-enhanced oil recovery (CO2-EOR) method. In this study, CO2 storage potential, long-term CO2 fate and oil recovery potential in ROZs are characterized based on a reservoir model for Goldsmith-Landreth San Andres Unit in the Permian Basin. The effects of CO2 injection rates, well patterns (five-spot and line-drive), well spacings, injection modes (continuous CO2 injection and water-alternating-gas injection) on the CO2 retention in the reservoir and the oil production are investigated. After the preliminary assessment of CO2 storage and EOR potentials in ROZs, we next develop a novel approach based on a newly developed optimization algorithm-Stochastic Simplex Approximate Gradient (StoSAG) and predictive empirical models constructed using machine learning technique to co-optimize CO2 storage and oil recovery in ROZs. The performance of co-optimization of CO2 storage and oil recovery is compared with the performance of optimization of only CO2 storage.