IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v331y2023ics0306261922017019.html
   My bibliography  Save this article

An integrated multi-scale model for CO2 transport and storage in shale reservoirs

Author

Listed:
  • Wang, Yanwei
  • Dai, Zhenxue
  • Chen, Li
  • Shen, Xudong
  • Chen, Fangxuan
  • Soltanian, Mohamad Reza

Abstract

Shale reservoirs have gradually been recognized as promising potential candidates for CO2 geological storage due to their wide geographic distribution and enormous storage potential. CO2 sequestration in shale is a complex multi-scale process with spatial sizes ranging from nano-scale to kilometer-scale. In this study, an integrated multi-scale model, considering CO2 adsorption, dissolution, slip flow, and diffusion, is developed to describe CO2 transport and storage in shale reservoirs with a multi-stage fractured horizontal well. The computationally efficient semi-analytical solution of the multi-scale model is obtained via Laplace transformation and Pedrosa’s substitution. Based on the established model, a workflow for evaluating the CO2 storage capacity of shale reservoirs is proposed. Taking the Longmaxi shale reservoir in the Sichuan Basin as an application case, the results show that the storage capacity can reach up to 29.97 × 108 m3 at a high constrained injection pressure. In addition, sensitivity analysis suggests that CO2 storage capacity is strongly influenced by the adsorption index of clay minerals, kerogen content, solubility coefficient, and inter-porosity flow coefficient of kerogen and inorganic matrix. Most reservoir parameters have negligible effects on storage capacity at low injection pressures, yet significant effects at high injection pressures. More specifically, the CO2 storage capacity can increase by 6.8 folds when the constrained injection pressure is increased from 5.5 to 8.5 MPa. The findings obtained in this study further expand the description of CO2 transport in the shale formation, laying the foundation for highly efficient CO2 sequestration in shale reservoirs.

Suggested Citation

  • Wang, Yanwei & Dai, Zhenxue & Chen, Li & Shen, Xudong & Chen, Fangxuan & Soltanian, Mohamad Reza, 2023. "An integrated multi-scale model for CO2 transport and storage in shale reservoirs," Applied Energy, Elsevier, vol. 331(C).
  • Handle: RePEc:eee:appene:v:331:y:2023:i:c:s0306261922017019
    DOI: 10.1016/j.apenergy.2022.120444
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922017019
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2022.120444?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Yueliang & Rui, Zhenhua & Yang, Tao & Dindoruk, Birol, 2022. "Using propanol as an additive to CO2 for improving CO2 utilization and storage in oil reservoirs," Applied Energy, Elsevier, vol. 311(C).
    2. Gao, Qi & Han, Songcai & Cheng, Yuanfang & Shi, Xian & Yan, Chuanliang & Han, Zhongying, 2022. "Flow-coupled-geomechanical modelling of CO2 transport in depleted shale from a microscopic perspective," Energy, Elsevier, vol. 257(C).
    3. Tayari, Farid & Blumsack, Seth, 2020. "A real options approach to production and injection timing under uncertainty for CO2 sequestration in depleted shale gas reservoirs," Applied Energy, Elsevier, vol. 263(C).
    4. Camilo Mora & Daniele Spirandelli & Erik C. Franklin & John Lynham & Michael B. Kantar & Wendy Miles & Charlotte Z. Smith & Kelle Freel & Jade Moy & Leo V. Louis & Evan W. Barba & Keith Bettinger & Ab, 2018. "Broad threat to humanity from cumulative climate hazards intensified by greenhouse gas emissions," Nature Climate Change, Nature, vol. 8(12), pages 1062-1071, December.
    5. Kim, Tae Hong & Cho, Jinhyung & Lee, Kun Sang, 2017. "Evaluation of CO2 injection in shale gas reservoirs with multi-component transport and geomechanical effects," Applied Energy, Elsevier, vol. 190(C), pages 1195-1206.
    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).
    7. Kou, Zuhao & Wang, Tongtong & Chen, Zhuoting & Jiang, Jincheng, 2021. "A fast and reliable methodology to evaluate maximum CO2 storage capacity of depleted coal seams: A case study," Energy, Elsevier, vol. 231(C).
    8. Guo, Wei & Yang, Qinchuan & Deng, Sunhua & Li, Qiang & Sun, Youhong & Su, Jianzheng & Zhu, Chaofan, 2022. "Experimental study of the autothermic pyrolysis in-situ conversion process (ATS) for oil shale recovery," Energy, Elsevier, vol. 258(C).
    9. Chen, Siyuan & Liu, Jiangfeng & Zhang, Qi & Teng, Fei & McLellan, Benjamin C., 2022. "A critical review on deployment planning and risk analysis of carbon capture, utilization, and storage (CCUS) toward carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    10. Xiao, Kun & Yu, Bolin & Cheng, Lei & Li, Fei & Fang, Debin, 2022. "The effects of CCUS combined with renewable energy penetration under the carbon peak by an SD-CGE model: Evidence from China," Applied Energy, Elsevier, vol. 321(C).
    11. Wang, Hui & Chen, Li & Qu, Zhiguo & Yin, Ying & Kang, Qinjun & Yu, Bo & Tao, Wen-Quan, 2020. "Modeling of multi-scale transport phenomena in shale gas production — A critical review," Applied Energy, Elsevier, vol. 262(C).
    12. Huang, Jingwei & Jin, Tianying & Barrufet, Maria & Killough, John, 2020. "Evaluation of CO2 injection into shale gas reservoirs considering dispersed distribution of kerogen," Applied Energy, Elsevier, vol. 260(C).
    13. Hu, Yuhao & Liu, Guannan & Luo, Ning & Gao, Feng & Yue, Fengtian & Gao, Tao, 2022. "Multi-field coupling deformation of rock and multi-scale flow of gas in shale gas extraction," Energy, Elsevier, vol. 238(PA).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wei, Jianguang & Yang, Erlong & Li, Jiangtao & Liang, Shuang & Zhou, Xiaofeng, 2023. "Nuclear magnetic resonance study on the evolution of oil water distribution in multistage pore networks of shale oil reservoirs," Energy, Elsevier, vol. 282(C).
    2. Wang, Chao & Liu, Bo & Mohammadi, Mohammad-Reza & Fu, Li & Fattahi, Elham & Motra, Hem Bahadur & Hazra, Bodhisatwa & Hemmati-Sarapardeh, Abdolhossein & Ostadhassan, Mehdi, 2024. "Integrating experimental study and intelligent modeling of pore evolution in the Bakken during simulated thermal progression for CO2 storage goals," Applied Energy, Elsevier, vol. 359(C).

    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. 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.
    2. Rahmad Syah & Seyed Mehdi Alizadeh & Karina Shamilyevna Nurgalieva & John William Grimaldo Guerrero & Mahyuddin K. M. Nasution & Afshin Davarpanah & Dadan Ramdan & Ahmed Sayed M. Metwally, 2021. "A Laboratory Approach to Measure Enhanced Gas Recovery from a Tight Gas Reservoir during Supercritical Carbon Dioxide Injection," Sustainability, MDPI, vol. 13(21), pages 1-14, October.
    3. Micheal, Marembo & Yu, Hao & Meng, SiWei & Xu, WenLong & Huang, HanWei & Huang, MengCheng & Zhang, HouLin & Liu, He & Wu, HengAn, 2023. "Gas production from shale reservoirs with bifurcating fractures: A modified quadruple-domain model coupling microseismic events," Energy, Elsevier, vol. 278(C).
    4. Yang, Run & Liu, Xiangui & Yu, Rongze & Hu, Zhiming & Duan, Xianggang, 2022. "Long short-term memory suggests a model for predicting shale gas production," Applied Energy, Elsevier, vol. 322(C).
    5. Hou, Lei & Elsworth, Derek & Wang, Jintang & Zhou, Junping & Zhang, Fengshou, 2024. "Feasibility and prospects of symbiotic storage of CO2 and H2 in shale reservoirs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    6. Li, Dafang & Sun, Weifu & Luo, Zhenmin, 2023. "Methane deflagration promoted by enhancing ignition efficiency via hydrogen doping, with a view to fracturing shales," Energy, Elsevier, vol. 282(C).
    7. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
    8. Dazhong Ren & Zhendong Wang & Fu Yang & Hao Zeng & Chenyuan Lü & Han Wang & Senhao Wang & Shaotao Xu, 2024. "Study on the Applicability of Autothermic Pyrolysis In Situ Conversion Process for Low-Grade Oil Shale: A Case Study of Tongchuan, Ordos Basin, China," Energies, MDPI, vol. 17(13), pages 1-21, June.
    9. Pan, Xuwei & Wu, Yan & Li, Tingzhen & Lan, Guoxin & Shen, Jia & Yu, Yue & Xue, Ping & Chen, Dan & Wang, Maoqing & Fu, Chuan, 2023. "A study of co-pyrolysis of sewage sludge and rice husk for syngas production based on a cyclic catalytic integrated process system," Renewable Energy, Elsevier, vol. 215(C).
    10. Wang, Junbo & Ma, Zhenyu & Fan, Xiayang, 2023. "We are all in the same boat: The welfare and carbon abatement effects of the EU carbon border adjustment mechanism," MPRA Paper 118978, University Library of Munich, Germany.
    11. Liu, Feng & van den Bergh, Jeroen & Wei, Yihang, 2024. "Testing mechanisms through which China's ETS promotes a low-carbon transition," Energy Economics, Elsevier, vol. 132(C).
    12. Tan, Qinliang & Han, Jian & Liu, Yuan, 2023. "Examining the synergistic diffusion process of carbon capture and renewable energy generation technologies under market environment: A multi-agent simulation analysis," Energy, Elsevier, vol. 282(C).
    13. Cormos, Calin-Cristian, 2023. "Green hydrogen production from decarbonized biomass gasification: An integrated techno-economic and environmental analysis," Energy, Elsevier, vol. 270(C).
    14. Huang, Liang & Ning, Zhengfu & Wang, Qing & Zhang, Wentong & Cheng, Zhilin & Wu, Xiaojun & Qin, Huibo, 2018. "Effect of organic type and moisture on CO2/CH4 competitive adsorption in kerogen with implications for CO2 sequestration and enhanced CH4 recovery," Applied Energy, Elsevier, vol. 210(C), pages 28-43.
    15. Nguyen, Phong & Carey, J. William & Viswanathan, Hari S. & Porter, Mark, 2018. "Effectiveness of supercritical-CO2 and N2 huff-and-puff methods of enhanced oil recovery in shale fracture networks using microfluidic experiments," Applied Energy, Elsevier, vol. 230(C), pages 160-174.
    16. Anita Punia, 2021. "Carbon dioxide sequestration by mines: implications for climate change," Climatic Change, Springer, vol. 165(1), pages 1-17, March.
    17. Jin, Lu & Hawthorne, Steven & Sorensen, James & Pekot, Lawrence & Kurz, Bethany & Smith, Steven & Heebink, Loreal & Herdegen, Volker & Bosshart, Nicholas & Torres, José & Dalkhaa, Chantsalmaa & Peters, 2017. "Advancing CO2 enhanced oil recovery and storage in unconventional oil play—Experimental studies on Bakken shales," Applied Energy, Elsevier, vol. 208(C), pages 171-183.
    18. Xuhua Gao & Junhong Yu & Xinchun Shang & Weiyao Zhu, 2023. "Investigation on Nonlinear Behaviors of Seepage in Deep Shale Gas Reservoir with Viscoelasticity," Energies, MDPI, vol. 16(17), pages 1-23, August.
    19. McLaughlin, Hope & Littlefield, Anna A. & Menefee, Maia & Kinzer, Austin & Hull, Tobias & Sovacool, Benjamin K. & Bazilian, Morgan D. & Kim, Jinsoo & Griffiths, Steven, 2023. "Carbon capture utilization and storage in review: Sociotechnical implications for a carbon reliant world," Renewable and Sustainable Energy Reviews, Elsevier, vol. 177(C).
    20. Mazahir Hussain & Shuang Liu & Umar Ashraf & Muhammad Ali & Wakeel Hussain & Nafees Ali & Aqsa Anees, 2022. "Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type," Energies, MDPI, vol. 15(12), pages 1-15, June.

    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:eee:appene:v:331:y:2023:i:c:s0306261922017019. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.