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Geochemistry in Geological CO 2 Sequestration: A Comprehensive Review

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  • Jemal Worku Fentaw

    (Bob L. Herd Department of Petroleum Engineering, Texas Tech University, Lubbock, TX 79409, USA)

  • Hossein Emadi

    (Bob L. Herd Department of Petroleum Engineering, Texas Tech University, Lubbock, TX 79409, USA)

  • Athar Hussain

    (Bob L. Herd Department of Petroleum Engineering, Texas Tech University, Lubbock, TX 79409, USA)

  • Diana Maury Fernandez

    (Bob L. Herd Department of Petroleum Engineering, Texas Tech University, Lubbock, TX 79409, USA)

  • Sugan Raj Thiyagarajan

    (Bob L. Herd Department of Petroleum Engineering, Texas Tech University, Lubbock, TX 79409, USA)

Abstract

The increasing level of anthropogenic CO 2 in the atmosphere has made it imperative to investigate an efficient method for carbon sequestration. Geological carbon sequestration presents a viable path to mitigate greenhouse gas emissions by sequestering the captured CO 2 deep underground in rock formations to store it permanently. Geochemistry, as the cornerstone of geological CO 2 sequestration (GCS), plays an indispensable role. Therefore, it is not just timely but also urgent to undertake a comprehensive review of studies conducted in this area, articulate gaps and findings, and give directions for future research areas. This paper reviews geochemistry in terms of the sequestration of CO 2 in geological formations, addressing mechanisms of trapping, challenges, and ways of mitigating challenges in trapping mechanisms; mineralization and methods of accelerating mineralization; and the interaction between rock, brine, and CO 2 for the long-term containment and storage of CO 2 . Mixing CO 2 with brine before or during injection, using microbes, selecting sedimentary reservoirs with reactive minerals, co-injection of carbonate anhydrase, and enhancing the surface area of reactive minerals are some of the mechanisms used to enhance mineral trapping in GCS applications. This review also addresses the potential challenges and opportunities associated with geological CO 2 storage. Challenges include caprock integrity, understanding the lasting effects of storing CO 2 on geological formations, developing reliable models for monitoring CO 2 –brine–rock interactions, CO 2 impurities, and addressing public concerns about safety and environmental impacts. Conversely, opportunities in the sequestration of CO 2 lie in the vast potential for storing CO 2 in geological formations like depleted oil and gas reservoirs, saline aquifers, coal seams, and enhanced oil recovery (EOR) sites. Opportunities include improved geochemical trapping of CO 2 , optimized storage capacity, improved sealing integrity, managed wellbore leakage risk, and use of sealant materials to reduce leakage risk. Furthermore, the potential impact of advancements in geochemical research, understanding geochemical reactions, addressing the challenges, and leveraging the opportunities in GCS are crucial for achieving sustainable carbon mitigation and combating global warming effectively.

Suggested Citation

  • Jemal Worku Fentaw & Hossein Emadi & Athar Hussain & Diana Maury Fernandez & Sugan Raj Thiyagarajan, 2024. "Geochemistry in Geological CO 2 Sequestration: A Comprehensive Review," Energies, MDPI, vol. 17(19), pages 1-35, October.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:5000-:d:1493973
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    References listed on IDEAS

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    1. Li, Sihai & Zhang, Shicheng & Xing, Huilin & Zou, Yushi, 2022. "CO2–brine–rock interactions altering the mineralogical, physical, and mechanical properties of carbonate-rich shale oil reservoirs," Energy, Elsevier, vol. 256(C).
    2. Cheng Cao & Hejuan Liu & Zhengmeng Hou & Faisal Mehmood & Jianxing Liao & Wentao Feng, 2020. "A Review of CO 2 Storage in View of Safety and Cost-Effectiveness," Energies, MDPI, vol. 13(3), pages 1-45, January.
    3. Chen, Bailian & Harp, Dylan R. & Lin, Youzuo & Keating, Elizabeth H. & Pawar, Rajesh J., 2018. "Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach," Applied Energy, Elsevier, vol. 225(C), pages 332-345.
    4. Enbin Liu & Xudong Lu & Daocheng Wang, 2023. "A Systematic Review of Carbon Capture, Utilization and Storage: Status, Progress and Challenges," Energies, MDPI, vol. 16(6), pages 1-48, March.
    5. Aminu, Mohammed D. & Nabavi, Seyed Ali & Rochelle, Christopher A. & Manovic, Vasilije, 2017. "A review of developments in carbon dioxide storage," Applied Energy, Elsevier, vol. 208(C), pages 1389-1419.
    6. Liu, Quanyou & Zhu, Dongya & Jin, Zhijun & Tian, Hailong & Zhou, Bing & Jiang, Peixue & Meng, Qingqiang & Wu, Xiaoqi & Xu, Huiyuan & Hu, Ting & Zhu, Huixing, 2023. "Carbon capture and storage for long-term and safe sealing with constrained natural CO2 analogs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
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