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Reservoir simulation of carbon storage associated with CO2 EOR in residual oil zones, San Andres formation of West Texas, Permian Basin, USA

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  • Ren, Bo
  • Duncan, Ian J.

Abstract

Residual oil zones (ROZs) are reservoirs in which oil is largely at levels of residual saturation. Such reservoirs cannot be produced by conventional techniques; rather some forms of enhanced oil recovery (EOR), such as CO2 injection is required. As a result, these zones have a potential for CO2 storage associated with EOR activities. In West Texas, the oil production potential of these zones, associated with the San Andres Formation alone, has been estimated as on the order of tens of billions of barrels. A series of numerical simulations of CO2 miscible flooding were conducted on 11 Sub-Volumes cut from a larger static reservoir that represents the range of heterogeneity in permeability and porosity found in San Andres ROZs. This work set out to evaluate the effects of injection strategies and reservoir heterogeneities on the performance of CO2 sequestration. The injection techniques investigated were: continuous CO2 injection and water alternating gas (WAG). Multiple factors were examined, including domain boundary conditions, well patterns, injection rates, permeability anisotropies, and natural fractures. It was found that ROZs could have higher retention fractions (i.e., volume fraction of injected CO2 retained in ROZs) for a combination of inverted five-spot well patterns and large WAG ratios. Based on the results of these numerical simulations, the long-term potential for CO2 storage associated with CO2-EOR of ROZs can be assessed. Our results provide key insights into how future CO2 storage projects associated with EOR in ROZs within carbonate sequences may be implemented.

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  • Ren, Bo & Duncan, Ian J., 2019. "Reservoir simulation of carbon storage associated with CO2 EOR in residual oil zones, San Andres formation of West Texas, Permian Basin, USA," Energy, Elsevier, vol. 167(C), pages 391-401.
    • Handle: RePEc:eee:energy:v:167:y:2019:i:c:p:391-401
    DOI: 10.1016/j.energy.2018.11.007
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    References listed on IDEAS

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    1. Zuloaga, Pavel & Yu, Wei & Miao, Jijun & Sepehrnoori, Kamy, 2017. "Performance evaluation of CO2 Huff-n-Puff and continuous CO2 injection in tight oil reservoirs," Energy, Elsevier, vol. 134(C), pages 181-192.
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    Cited by:

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    3. Tang, Yong & Chen, Yulin & He, Youwei & Yu, Guangming & Guo, Xifeng & Yang, Qing & Wang, Yong, 2021. "An improved system for evaluating the adaptability of natural gas flooding in enhancing oil recovery considering the miscible ability," Energy, Elsevier, vol. 236(C).
    4. Maja Arnaut & Domagoj Vulin & Gabriela José García Lamberg & Lucija Jukić, 2021. "Simulation Analysis of CO 2 -EOR Process and Feasibility of CO 2 Storage during EOR," Energies, MDPI, vol. 14(4), pages 1-28, February.
    5. Xiao, Caiyun & Ni, Hongjian & Shi, Xian, 2022. "Unsteady model for wellbore pressure transmission of carbon dioxide fracturing considering limited-flow outlet," Energy, Elsevier, vol. 239(PE).
    6. Amirsardari, Mahdi & Afsari, Khalil, 2024. "Pore-to-field scale modeling of residual gas trapping in tight carbonate underground gas reservoirs," Energy, Elsevier, vol. 298(C).
    7. Ren, Bo & Male, Frank & Duncan, Ian J., 2022. "Economic analysis of CCUS: Accelerated development for CO2 EOR and storage in residual oil zones under the context of 45Q tax credit," Applied Energy, Elsevier, vol. 321(C).
    8. Cai, Mingyu & Su, Yuliang & Elsworth, Derek & Li, Lei & Fan, Liyao, 2021. "Hydro-mechanical-chemical modeling of sub-nanopore capillary-confinement on CO2-CCUS-EOR," Energy, Elsevier, vol. 225(C).
    9. Ren, Bo & Duncan, Ian J., 2021. "Maximizing oil production from water alternating gas (CO2) injection into residual oil zones: The impact of oil saturation and heterogeneity," Energy, Elsevier, vol. 222(C).
    10. Yang, Renfeng & Zhang, Jinqing & Chen, Han & Jiang, Ruizhong & Sun, Zhe & Rui, Zhenhua, 2019. "The injectivity variation prediction model for water flooding oilfields sustainable development," Energy, Elsevier, vol. 189(C).
    11. Vo Thanh, Hung & Lee, Kang-Kun, 2022. "Application of machine learning to predict CO2 trapping performance in deep saline aquifers," Energy, Elsevier, vol. 239(PE).
    12. Zhang, Kai & Lau, Hon Chung & Bokka, Harsha Kumar & Hadia, Nanji J., 2022. "Decarbonizing the power and industry sectors in India by carbon capture and storage," Energy, Elsevier, vol. 249(C).
    13. Steele, Amanda Harker & Sharma, Smriti & Pena Cabra, Ivonne & Clahane, Luke & Iyengar, Arun, 2023. "A tool for measuring the system cost of replacement energy," Energy, Elsevier, vol. 275(C).
    14. Alirza Orujov & Kipp Coddington & Saman A. Aryana, 2023. "A Review of CCUS in the Context of Foams, Regulatory Frameworks and Monitoring," Energies, MDPI, vol. 16(7), pages 1-41, April.

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