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Title: 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-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.

Authors:
ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1558978
Alternate Identifier(s):
OSTI ID: 1557015
Report Number(s):
LA-UR-19-24764
Journal ID: ISSN 0920-4105
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Petroleum Science and Engineering
Additional Journal Information:
Journal Volume: 182; Journal Issue: C; Journal ID: ISSN 0920-4105
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; Earth Sciences

Citation Formats

Chen, Bailian, and Pawar, Rajesh J. Capacity assessment and co-optimization of CO2 storage and enhanced oil recovery in residual oil zones. United States: N. p., 2019. Web. doi:10.1016/j.petrol.2019.106342.
Chen, Bailian, & Pawar, Rajesh J. Capacity assessment and co-optimization of CO2 storage and enhanced oil recovery in residual oil zones. United States. doi:10.1016/j.petrol.2019.106342.
Chen, Bailian, and Pawar, Rajesh J. Thu . "Capacity assessment and co-optimization of CO2 storage and enhanced oil recovery in residual oil zones". United States. doi:10.1016/j.petrol.2019.106342. https://www.osti.gov/servlets/purl/1558978.
@article{osti_1558978,
title = {Capacity assessment and co-optimization of CO2 storage and enhanced oil recovery in residual oil zones},
author = {Chen, Bailian and Pawar, Rajesh J.},
abstractNote = {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.},
doi = {10.1016/j.petrol.2019.106342},
journal = {Journal of Petroleum Science and Engineering},
number = C,
volume = 182,
place = {United States},
year = {2019},
month = {8}
}

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