IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v183y2019icp291-304.html
   My bibliography  Save this article

Characterization of CO2 storage and enhanced oil recovery in residual oil zones

Author

Listed:
  • Chen, Bailian
  • Pawar, Rajesh J.

Abstract

Residual oil zones (ROZs) are reservoirs in which oil is swept over geologic time period and exists at residual saturation. The oil in such reservoirs cannot be commercially exploited using conventional oil recovery methods as the oil exists at residual oil saturation. Instead, enhanced oil recovery methods such as CO2 injection are required. Recently, ROZs have been increasingly studied as potential CO2 storage targets. In spite of increased interest in ROZs, there are significant gaps in the knowledge of parameters and processes that impact CO2 storage and oil recovery. In this work, we identify key geologic and operational characteristics that affect CO2 storage capacity and oil recovery potential by performing Monte Carlo simulations and sensitivity analysis. In addition to CO2 storage capacity, we also characterize the long-term CO2 fate in ROZs. The distinction of CO2 storage in ROZs from conventional oil reservoirs and saline aquifers are also characterized. Furthermore, predictive models based on machine learning techniques are developed to estimate CO2 storage and oil production potentials for ROZs. The applicability of the predictive models is demonstrated for five ROZs in the Permian Basin.

Suggested Citation

  • Chen, Bailian & Pawar, Rajesh J., 2019. "Characterization of CO2 storage and enhanced oil recovery in residual oil zones," Energy, Elsevier, vol. 183(C), pages 291-304.
    • Handle: RePEc:eee:energy:v:183:y:2019:i:c:p:291-304
    DOI: 10.1016/j.energy.2019.06.142
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219312745
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.06.142?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. Wang, Chang-Long & Cheng, Wen-Long & Nian, Yong-Le & Yang, Lei & Han, Bing-Bing & Liu, Ming-Hou, 2018. "Simulation of heat extraction from CO2-based enhanced geothermal systems considering CO2 sequestration," Energy, Elsevier, vol. 142(C), pages 157-167.
    2. Ren, Bo & Ren, Shaoran & Zhang, Liang & Chen, Guoli & Zhang, Hua, 2016. "Monitoring on CO2 migration in a tight oil reservoir during CCS-EOR in Jilin Oilfield China," Energy, Elsevier, vol. 98(C), pages 108-121.
    3. Luo, Feng & Xu, Rui-Na & Jiang, Pei-Xue, 2014. "Numerical investigation of fluid flow and heat transfer in a doublet enhanced geothermal system with CO2 as the working fluid (CO2–EGS)," Energy, Elsevier, vol. 64(C), pages 307-322.
    4. Cui, Guodong & Wang, Yi & Rui, Zhenhua & Chen, Bailian & Ren, Shaoran & Zhang, Liang, 2018. "Assessing the combined influence of fluid-rock interactions on reservoir properties and injectivity during CO2 storage in saline aquifers," Energy, Elsevier, vol. 155(C), pages 281-296.
    5. Zhang, Liang & Li, Xin & Zhang, Yin & Cui, Guodong & Tan, Chunyang & Ren, Shaoran, 2017. "CO2 injection for geothermal development associated with EGR and geological storage in depleted high-temperature gas reservoirs," Energy, Elsevier, vol. 123(C), pages 139-148.
    6. 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.
    7. Wang, Xiao & van ’t Veld, Klaas & Marcy, Peter & Huzurbazar, Snehalata & Alvarado, Vladimir, 2018. "Economic co-optimization of oil recovery and CO2 sequestration," Applied Energy, Elsevier, vol. 222(C), pages 132-147.
    8. 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.
    9. 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.
    10. van Bergen, F. & Gale, J. & Damen, K.J. & Wildenborg, A.F.B., 2004. "Worldwide selection of early opportunities for CO2-enhanced oil recovery and CO2-enhanced coal bed methane production," Energy, Elsevier, vol. 29(9), pages 1611-1621.
    11. Park, Chan-Hee & Lee, Seong Kon & Lee, Cholwoo & Kim, Seong-Kyun, 2018. "Applicability of thermal response tests for assessing in-situ CO2 storage in a saline aquifer," Energy, Elsevier, vol. 154(C), pages 210-220.
    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. Hung Vo Thanh & Sajad Ebrahimnia Taremsari & Benyamin Ranjbar & Hossein Mashhadimoslem & Ehsan Rahimi & Mohammad Rahimi & Ali Elkamel, 2023. "Hydrogen Storage on Porous Carbon Adsorbents: Rediscovery by Nature-Derived Algorithms in Random Forest Machine Learning Model," Energies, MDPI, vol. 16(5), pages 1-19, February.
    2. Sergey Fominykh & Stevan Stankovski & Vladimir M. Markovic & Dusko Petrovic & Sead Osmanović, 2023. "Analysis of CO 2 Migration in Horizontal Saline Aquifers during Carbon Capture and Storage Process," Sustainability, MDPI, vol. 15(11), pages 1-17, May.
    3. Yunqi Jiang & Huaqing Zhang & Kai Zhang & Jian Wang & Shiti Cui & Jianfa Han & Liming Zhang & Jun Yao, 2022. "Reservoir Characterization and Productivity Forecast Based on Knowledge Interaction Neural Network," Mathematics, MDPI, vol. 10(9), pages 1-22, May.
    4. 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).
    5. Huang, Xianfu & Zhao, Ya-Pu, 2023. "Evolution of pore structure and adsorption-desorption in oil shale formation rocks after compression," Energy, Elsevier, vol. 278(PA).
    6. Li, Zongfa & Liu, Jiahui & Su, Yuliang & Fan, Liyao & Hao, Yongmao & kanjibayi, Bahedawulieti & Huang, Lijuan & Ren, Shaoran & Sun, Yongquan & Liu, Ran, 2023. "Influences of diffusion and advection on dynamic oil-CO2 mixing during CO2 EOR and storage process: Experimental study and numerical modeling at pore-scales," Energy, Elsevier, vol. 267(C).
    7. Qureshi, M Fahed & Khandelwal, Himanshu & Usadi, Adam & Barckholtz, Timothy A. & Mhadeshwar, Ashish B. & Linga, Praveen, 2022. "CO2 hydrate stability in oceanic sediments under brine conditions," Energy, Elsevier, vol. 256(C).
    8. Kasala, Erasto E. & Wang, Jinjie & Lwazi, Hussein M. & Nyakilla, Edwin E. & Kibonye, John S., 2024. "The influence of hydraulic fracture and reservoir parameters on the storage of CO2 and enhancing CH4 recovery in Yanchang formation," Energy, Elsevier, vol. 296(C).
    9. Zhao, Yuechao & Zhang, Yuying & Lei, Xu & Zhang, Yi & Song, Yongchen, 2020. "CO2 flooding enhanced oil recovery evaluated using magnetic resonance imaging technique," Energy, Elsevier, vol. 203(C).
    10. 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).
    11. Cai, Mingyu & Su, Yuliang & Elsworth, Derek & Li, Lei & Fan, Liyao, 2021. "Hydro-mechanical-chemical modeling of sub-nanopore capillary-confinement on CO2-CCUS-EOR," Energy, Elsevier, vol. 225(C).
    12. Ren, Bo & Duncan, Ian J., 2021. "Maximizing oil production from water alternating gas (CO2) injection into residual oil zones: The impact of oil saturation and heterogeneity," Energy, Elsevier, vol. 222(C).
    13. Vo Thanh, Hung & Sheini Dashtgoli, Danial & Zhang, Hemeng & Min, Baehyun, 2023. "Machine-learning-based prediction of oil recovery factor for experimental CO2-Foam chemical EOR: Implications for carbon utilization projects," Energy, Elsevier, vol. 278(PA).
    14. 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).
    15. Zhang, Kai & Lau, Hon Chung & Bokka, Harsha Kumar & Hadia, Nanji J., 2022. "Decarbonizing the power and industry sectors in India by carbon capture and storage," Energy, Elsevier, vol. 249(C).
    16. Aaditya Khanal & Md Fahim Shahriar, 2022. "Physics-Based Proxy Modeling of CO 2 Sequestration in Deep Saline Aquifers," Energies, MDPI, vol. 15(12), pages 1-23, June.
    17. Cui, Guodong & Pei, Shufeng & Rui, Zhenhua & Dou, Bin & Ning, Fulong & Wang, Jiaqiang, 2021. "Whole process analysis of geothermal exploitation and power generation from a depleted high-temperature gas reservoir by recycling CO2," Energy, Elsevier, vol. 217(C).
    18. Steele, Amanda Harker & Sharma, Smriti & Pena Cabra, Ivonne & Clahane, Luke & Iyengar, Arun, 2023. "A tool for measuring the system cost of replacement energy," Energy, Elsevier, vol. 275(C).
    19. Zhang, Xue & Su, Yuliang & Li, Lei & Da, Qi'an & Hao, Yongmao & Wang, Wendong & Liu, Jiahui & Gao, Xiaogang & Zhao, An & Wang, Kaiyu, 2022. "Microscopic remaining oil initiation mechanism and formation damage of CO2 injection after waterflooding in deep reservoirs," Energy, Elsevier, vol. 248(C).
    20. Hao, Yongmao & Li, Zongfa & Su, Yuliang & Kong, Chuixian & Chen, Hong & Meng, Yang, 2022. "Experimental investigation of CO2 storage and oil production of different CO2 injection methods at pore-scale and core-scale," Energy, Elsevier, vol. 254(PB).

    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. Xiao, Ting & Chen, Ting & Ma, Zhiwei & Tian, Hailong & Meguerdijian, Saro & Chen, Bailian & Pawar, Rajesh & Huang, Lianjie & Xu, Tianfu & Cather, Martha & McPherson, Brian, 2024. "A review of risk and uncertainty assessment for geologic carbon storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    3. Jing, Jing & Yang, Yanlin & Cheng, Jianmei & Ding, Zhaojing & Wang, Dandan & Jing, Xianwen, 2023. "Analysis of the effect of formation dip angle and injection pressure on the injectivity and migration of CO2 during storage," Energy, Elsevier, vol. 280(C).
    4. Yanqing Wang & Liang Zhang & Shaoran Ren & Bo Ren & Bailian Chen & Jun Lu, 2020. "Identification of potential CO2 leakage pathways and mechanisms in oil reservoirs using fault tree analysis," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(2), pages 331-346, April.
    5. 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).
    6. Shi, Yu & Song, Xianzhi & Wang, Gaosheng & McLennan, John & Forbes, Bryan & Li, Xiaojiang & Li, Jiacheng, 2019. "Study on wellbore fluid flow and heat transfer of a multilateral-well CO2 enhanced geothermal system," Applied Energy, Elsevier, vol. 249(C), pages 14-27.
    7. Ren, Bo & Trevisan, Luca, 2020. "Characterization of local capillary trap clusters in storage aquifers," Energy, Elsevier, vol. 193(C).
    8. Abdoli, B. & Hooshmand, F. & MirHassani, S.A., 2023. "A novel stochastic programming model under endogenous uncertainty for the CCS-EOR planning problem," Applied Energy, Elsevier, vol. 338(C).
    9. Chen, Yun & Ma, Guowei & Wang, Huidong & Li, Tuo & Wang, Yang & Sun, Zizheng, 2020. "Optimizing heat mining strategies in a fractured geothermal reservoir considering fracture deformation effects," Renewable Energy, Elsevier, vol. 148(C), pages 326-337.
    10. Ding, Junfeng & Wang, Shimin, 2018. "2D modeling of well array operating enhanced geothermal system," Energy, Elsevier, vol. 162(C), pages 918-932.
    11. Ajoma, Emmanuel & Saira, & Sungkachart, Thanarat & Le-Hussain, Furqan, 2021. "Effect of miscibility and injection rate on water-saturated CO2 Injection," Energy, Elsevier, vol. 217(C).
    12. Chen, Guodong & Jiao, Jiu Jimmy & Jiang, Chuanyin & Luo, Xin, 2024. "Surrogate-assisted level-based learning evolutionary search for geothermal heat extraction optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    13. Ajoma, Emmanuel & Saira, & Sungkachart, Thanarat & Ge, Jiachao & Le-Hussain, Furqan, 2020. "Water-saturated CO2 injection to improve oil recovery and CO2 storage," Applied Energy, Elsevier, vol. 266(C).
    14. Wang, H.D. & Chen, Y. & Ma, G.W., 2020. "Effects of capillary pressures on two-phase flow of immiscible carbon dioxide enhanced oil recovery in fractured media," Energy, Elsevier, vol. 190(C).
    15. Cai, Mingyu & Su, Yuliang & Elsworth, Derek & Li, Lei & Fan, Liyao, 2021. "Hydro-mechanical-chemical modeling of sub-nanopore capillary-confinement on CO2-CCUS-EOR," Energy, Elsevier, vol. 225(C).
    16. Sun, Fengrui & Yao, Yuedong & Li, Guozhen & Li, Xiangfang, 2018. "Geothermal energy extraction in CO2 rich basin using abandoned horizontal wells," Energy, Elsevier, vol. 158(C), pages 760-773.
    17. Scanziani, Alessio & Singh, Kamaljit & Menke, Hannah & Bijeljic, Branko & Blunt, Martin J., 2020. "Dynamics of enhanced gas trapping applied to CO2 storage in the presence of oil using synchrotron X-ray micro tomography," Applied Energy, Elsevier, vol. 259(C).
    18. 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.
    19. 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.
    20. Zhang, Lisong & Zhang, Shiyan & Jiang, Weizhai & Wang, Zhiyuan & Li, Jing & Bian, Yinghui, 2018. "A mechanism of fluid exchange associated to CO2 leakage along activated fault during geologic storage," Energy, Elsevier, vol. 165(PB), pages 1178-1190.

    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:energy:v:183:y:2019:i:c:p:291-304. 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.journals.elsevier.com/energy .

    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.