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Enhancement of CO2 trapping efficiency in heterogeneous reservoirs by water‐alternating gas injection

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  • Emad A. Al‐Khdheeawi
  • Stephanie Vialle
  • Ahmed Barifcani
  • Mohammad Sarmadivaleh
  • Stefan Iglauer

Abstract

Carbon geosequestration efficiency is affected by various factors including reservoir heterogeneity, caprock properties, rock wettability, aquifer brine salinity and injection‐well configuration. Some parameters (e.g. the formation geology) cannot be changed but it is possible to select others and engineer an optimized storage program. One of these parameters is the way CO2 is injected into the reservoir. There are three main options for this: (1) continuous injection, (2) intermittent injection, and (3) water‐alternating gas (WAG) injection. We thus studied the efficiency of each injection option and predicted the associated storage capacities and CO2‐plume movements (which are related to the containment security). We found that WAG injection showed significantly superior behavior over continuous and intermittent injections (which resulted in similar CO2 flow responses). Specifically, residual and solubility trapping were significantly enhanced, whereas vertical CO2 migration was cut by approximately two‐thirds. Thus, in the examined reservoir, the WAG injection is preferred as it strongly enhances CO2 storage efficiency and containment security. © 2018 Society of Chemical Industry and John Wiley & Sons, Ltd.

Suggested Citation

  • Emad A. Al‐Khdheeawi & Stephanie Vialle & Ahmed Barifcani & Mohammad Sarmadivaleh & Stefan Iglauer, 2018. "Enhancement of CO2 trapping efficiency in heterogeneous reservoirs by water‐alternating gas injection," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 8(5), pages 920-931, October.
    • Handle: RePEc:wly:greenh:v:8:y:2018:i:5:p:920-931
    DOI: 10.1002/ghg.1805
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    1. David Adam, 2001. "The North Sea bubble," Nature, Nature, vol. 411(6837), pages 518-518, May.
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    Cited by:

    1. Vo Thanh, Hung & Yasin, Qamar & Al-Mudhafar, Watheq J. & Lee, Kang-Kun, 2022. "Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers," Applied Energy, Elsevier, vol. 314(C).
    2. 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).

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