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

Journal Article · 08 August 2019 · Journal of Petroleum Science and Engineering

DOI: https://doi.org/10.1016/j.petrol.2019.106342 · OSTI ID:1558978

Chen, Bailian  ^[1]; Pawar, Rajesh J.  ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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-alternatinggas 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.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Office of Fossil Energy (FE)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1558978
• Report Number(s): LA-UR--19-24764
• Journal Information: Journal of Petroleum Science and Engineering, Journal Name: Journal of Petroleum Science and Engineering Journal Issue: C Vol. 182; ISSN 0920-4105
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English