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Large scale modeling and assessment of the feasibility of CO2 storage onshore Abu Dhabi

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  • Ajayi, Temitope
  • Awolayo, Adedapo
  • Gomes, Jorge S.
  • Parra, Humberto
  • Hu, Jialiang

Abstract

Carbon Capture and Storage has become a widely recognized viable means to exterminate anthropogenic gases from the atmosphere. One major setback is finding the best storage candidate. Initial comparison of geologic structures in the Emirate of Abu Dhabi has identified saline aquifers as potential sites for the storage of carbon dioxide necessitating the need for detailed assessments. Therefore, we present in this study the results of feasibility studies to determine the prospects of long term injection of CO2 into the proposed aquifers. The aquifers assessed are the Simsima, Dammam, UER and Shuaiba formations. The assessment criteria employed include the estimation of storage capacities, prediction of plume migration, contribution of trapping mechanisms to storage and sensitivity analysis to different reservoir parameters. Geologic models of the aquifers have been created by combining information from wells, seismic, logs and numerical models have been made by upscaling the respective geologic models. The analytical estimates of storage capacities indicate capacities of 5 GT, 1.7 GT, 8 GT and 960 MT for the Simsima, Dammam, UER and Shuaiba aquifers respectively. Dissolution is the dominant trapping mechanism at early and late times via diffusion and convection respectively. The sensitivity analysis implied that the effect of salinity and relative permeability parameters is vital in the final deployment of the storage projects. The results presented here suggests good potential candidates exists for the long-term storage of CO2 onshore Abu Dhabi.

Suggested Citation

  • Ajayi, Temitope & Awolayo, Adedapo & Gomes, Jorge S. & Parra, Humberto & Hu, Jialiang, 2019. "Large scale modeling and assessment of the feasibility of CO2 storage onshore Abu Dhabi," Energy, Elsevier, vol. 185(C), pages 653-670.
    • Handle: RePEc:eee:energy:v:185:y:2019:i:c:p:653-670
    DOI: 10.1016/j.energy.2019.07.052
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    References listed on IDEAS

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    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. Zhou, Xiang & Li, Xiuluan & Shen, Dehuang & Shi, Lanxiang & Zhang, Zhien & Sun, Xinge & Jiang, Qi, 2022. "CO2 huff-n-puff process to enhance heavy oil recovery and CO2 storage: An integration study," Energy, Elsevier, vol. 239(PB).
    3. Mahmoodpour, Saeed & Amooie, Mohammad Amin & Rostami, Behzad & Bahrami, Flora, 2020. "Effect of gas impurity on the convective dissolution of CO2 in porous media," Energy, Elsevier, vol. 199(C).
    4. Tian, Weibing & Wu, Keliu & Chen, Zhangxin & Gao, Yanling & Li, Jing & Wang, Muyuan, 2022. "A relative permeability model considering nanoconfinement and dynamic contact angle effects for tight reservoirs," Energy, Elsevier, vol. 258(C).
    5. Ren, Bo & Trevisan, Luca, 2020. "Characterization of local capillary trap clusters in storage aquifers," Energy, Elsevier, vol. 193(C).
    6. 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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