IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i18p13620-d1238163.html
   My bibliography  Save this article

A Simulation Study on Evaluating the Influence of Impurities on Hydrogen Production in Geological Carbon Dioxide Storage

Author

Listed:
  • Seungmo Ko

    (Department of Energy and Mineral Resources Engineering, Kangwon National University, Samcheok 25913, Republic of Korea)

  • Sung-Min Kim

    (Department of Energy Resources and Chemical Engineering, Kangwon National University, Samcheok 25913, Republic of Korea)

  • Hochang Jang

    (Department of Energy Resources and Chemical Engineering, Kangwon National University, Samcheok 25913, Republic of Korea)

Abstract

In this study, we examined the effect of CO 2 injection into deep saline aquifers, considering impurities present in blue hydrogen production. A fluid model was designed for reservoir conditions with impurity concentrations of 3.5 and 20%. The results showed that methane caused density decreases of 95.16 and 76.16% at 3.5 and 20%, respectively, whereas H 2 S caused decreases of 99.56 and 98.77%, respectively. Viscosity decreased from 0.045 to 0.037 cp with increasing methane content up to 20%; however, H 2 S did not affect the viscosity. Notably, CO 2 with H 2 S impacted these properties less than methane. Our simulation model was based on the Gorae-V properties and simulated injections for 10 years, followed by 100 years of monitoring. Compared with the pure CO 2 injection, methane reached its maximum pressure after eight years and eleven months at 3.5% and eight years at 20%, whereas H 2 S reached maximum pressure after nine years and two months and nine years and six months, respectively. These timings affected the amount of CO 2 injected. With methane as an impurity, injection efficiency decreased up to 73.16%, whereas with H 2 S, it decreased up to 81.99% with increasing impurity concentration. The efficiency of CO 2 storage in the dissolution and residual traps was analyzed to examine the impact of impurities. The residual trap efficiency consistently decreased with methane but increased with H 2 S. At 20% concentration, the methane trap exhibited higher efficiency at the end of injection; however, H 2 S had a higher efficiency at the monitoring endpoint. In carbon capture and storage projects, methane impurities require removal, whereas H 2 S may not necessitate desulfurization due to its minimal impact on CO 2 storage efficiency. Thus, the application of carbon capture and storage (CCS) to CO 2 emissions containing H 2 S as an impurity may enable economically viable operations by reducing additional costs.

Suggested Citation

  • Seungmo Ko & Sung-Min Kim & Hochang Jang, 2023. "A Simulation Study on Evaluating the Influence of Impurities on Hydrogen Production in Geological Carbon Dioxide Storage," Sustainability, MDPI, vol. 15(18), pages 1-19, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13620-:d:1238163
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/18/13620/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/18/13620/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Zhiyu & Wang, Jinsheng & Lan, Christopher & He, Ian & Ko, Vivien & Ryan, David & Wigston, Andrew, 2016. "A study on the impact of SO2 on CO2 injectivity for CO2 storage in a Canadian saline aquifer," Applied Energy, Elsevier, vol. 184(C), pages 329-336.
    2. Li, Didi & He, Yao & Zhang, Hongcheng & Xu, Wenbin & Jiang, Xi, 2017. "A numerical study of the impurity effects on CO2 geological storage in layered formation," Applied Energy, Elsevier, vol. 199(C), pages 107-120.
    3. Kim, Youngmin & Jang, Hochang & Kim, Junggyun & Lee, Jeonghwan, 2017. "Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network," Applied Energy, Elsevier, vol. 185(P1), pages 916-928.
    4. Wiesław Szott & Piotr Łętkowski & Andrzej Gołąbek & Krzysztof Miłek, 2020. "Modelling of the Long-Term Acid Gas Sequestration and Its Prediction: A Unique Case Study," Energies, MDPI, vol. 13(18), pages 1-24, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. You, Junyu & Ampomah, William & Sun, Qian, 2020. "Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework," Applied Energy, Elsevier, vol. 279(C).
    2. Dai, Zhenxue & Zhang, Ye & Bielicki, Jeffrey & Amooie, Mohammad Amin & Zhang, Mingkan & Yang, Changbing & Zou, Youqin & Ampomah, William & Xiao, Ting & Jia, Wei & Middleton, Richard & Zhang, Wen & Sun, 2018. "Heterogeneity-assisted carbon dioxide storage in marine sediments," Applied Energy, Elsevier, vol. 225(C), pages 876-883.
    3. Li, Didi & Zhang, Hongcheng & Li, Yang & Xu, Wenbin & Jiang, Xi, 2018. "Effects of N2 and H2S binary impurities on CO2 geological storage in stratified formation – A sensitivity study," Applied Energy, Elsevier, vol. 229(C), pages 482-492.
    4. Ampomah, W. & Balch, R.S. & Cather, M. & Will, R. & Gunda, D. & Dai, Z. & Soltanian, M.R., 2017. "Optimum design of CO2 storage and oil recovery under geological uncertainty," Applied Energy, Elsevier, vol. 195(C), pages 80-92.
    5. 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).
    6. Mehrdad Massoudi, 2021. "Mathematical Modeling of Fluid Flow and Heat Transfer in Petroleum Industries and Geothermal Applications 2020," Energies, MDPI, vol. 14(16), pages 1-4, August.
    7. Kamal Jawher Khudaida & Diganta Bhusan Das, 2020. "A Numerical Analysis of the Effects of Supercritical CO 2 Injection on CO 2 Storage Capacities of Geological Formations," Clean Technol., MDPI, vol. 2(3), pages 1-32, September.
    8. Wang, Sijia & Jiang, Lanlan & Cheng, Zucheng & Liu, Yu & Zhao, Jiafei & Song, Yongchen, 2021. "Experimental study on the CO2-decane displacement front behavior in high permeability sand evaluated by magnetic resonance imaging," Energy, Elsevier, vol. 217(C).
    9. Turgay Ertekin & Qian Sun, 2019. "Artificial Intelligence Applications in Reservoir Engineering: A Status Check," Energies, MDPI, vol. 12(15), pages 1-22, July.
    10. 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).
    11. Nekrasov, S., 2023. "Environmental management from the point of energy transition: The example of the Rybinsk reservoir," Journal of the New Economic Association, New Economic Association, vol. 61(4), pages 110-126.
    12. Anderson, Austin & Rezaie, Behnaz, 2019. "Geothermal technology: Trends and potential role in a sustainable future," Applied Energy, Elsevier, vol. 248(C), pages 18-34.
    13. Li, Wei & Lu, Can & Ding, Yi & Zhang, Yan-Wu, 2017. "The impacts of policy mix for resolving overcapacity in heavy chemical industry and operating national carbon emission trading market in China," Applied Energy, Elsevier, vol. 204(C), pages 509-524.
    14. Xie, Candie & Liu, Jingyong & Zhang, Xiaochun & Xie, Wuming & Sun, Jian & Chang, Kenlin & Kuo, Jiahong & Xie, Wenhao & Liu, Chao & Sun, Shuiyu & Buyukada, Musa & Evrendilek, Fatih, 2018. "Co-combustion thermal conversion characteristics of textile dyeing sludge and pomelo peel using TGA and artificial neural networks," Applied Energy, Elsevier, vol. 212(C), pages 786-795.
    15. Morgan, Joshua C. & Chinen, Anderson Soares & Anderson-Cook, Christine & Tong, Charles & Carroll, John & Saha, Chiranjib & Omell, Benjamin & Bhattacharyya, Debangsu & Matuszewski, Michael & Bhat, K. S, 2020. "Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO2 capture process," Applied Energy, Elsevier, vol. 262(C).
    16. 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).
    17. Chen, Long & Xu, Guiyin & Rui, Zhenhua & Alshawabkeh, Akram N., 2019. "Demonstration of a feasible energy-water-environment nexus: Waste sulfur dioxide for water treatment," Applied Energy, Elsevier, vol. 250(C), pages 1011-1022.
    18. Zhao, Zhi & Lu, Hai-Feng, 2023. "Deep learning interprets failure process of coal reservoir during CO2-desorption by 3D reconstruction techniques," Energy, Elsevier, vol. 282(C).
    19. Muhammad Hammad Rasool & Maqsood Ahmad & Muhammad Ayoub, 2023. "Selecting Geological Formations for CO 2 Storage: A Comparative Rating System," Sustainability, MDPI, vol. 15(8), pages 1-39, April.
    20. Kim, Min & Kwon, Seoyoon & Ji, Minsoo & Shin, Hyundon & Min, Baehyun, 2023. "Multi-lateral horizontal well with dual-tubing system to improve CO2 storage security and reduce CCS cost," Applied Energy, Elsevier, vol. 330(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13620-:d:1238163. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.