IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i16p3979-d1454199.html
   My bibliography  Save this article

Assessment of CO 2 Sequestration Capacity in a Low-Permeability Oil Reservoir Using Machine Learning Methods

Author

Listed:
  • Zuochun Fan

    (Institute of Advanced Studies, China University of Geosciences (Wuhan), Wuhan 430074, China
    Research Institute of Exploration and Development, Liaohe Oilfield Company, PetroChina, Panjin 124010, China)

  • Mei Tian

    (Research Institute of Exploration and Development, Liaohe Oilfield Company, PetroChina, Panjin 124010, China)

  • Man Li

    (Research Institute of Exploration and Development, Liaohe Oilfield Company, PetroChina, Panjin 124010, China)

  • Yidi Mi

    (Research Institute of Exploration and Development, Liaohe Oilfield Company, PetroChina, Panjin 124010, China)

  • Yue Jiang

    (Research Institute of Exploration and Development, Liaohe Oilfield Company, PetroChina, Panjin 124010, China)

  • Tao Song

    (State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China)

  • Jinxin Cao

    (State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China)

  • Zheyu Liu

    (State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China)

Abstract

The CO 2 sequestration capacity evaluation of reservoirs is a critical procedure for carbon capture, utilization, and storage (CCUS) techniques. However, calculating the sequestration amount for CO 2 flooding in low-permeability reservoirs is challenging. Herein, a method combining numerical simulation technology with artificial intelligence is proposed. Based on the typical geological and fluid characteristics of low-permeability oil reservoirs in the Liaohe oilfield, the CMG 2020 version software GEM module is used to establish a model for CO 2 flooding and sequestration. Meanwhile, a calculation method for the effective sequestration coefficient of CO 2 is established. We systematically study the sequestration rules in low-permeability reservoirs under varying conditions of permeability, reservoir temperature, and initial reservoir pressure. The results indicate that, as the permeability and sequestration pressure of the reservoir increase, oil recovery gradually increases. The proportion of structurally bound sequestration volume increases from 55% to 60%. Reservoir temperature has minimal impact on both the recovery rate and the improvement in sequestration efficiency. Sequestration pressure primarily improves sequestration efficiency by increasing the dissolution of CO 2 in the remaining oil and water. The calculation chart for the effective sequestration coefficient, developed using artificial intelligence algorithms under multi-factor conditions, enables accurate and rapid evaluation of the sequestration potential and the identification of favorable sequestration areas in low-permeability reservoirs. This approach provides valuable technical support for CO 2 flooding and sequestration in pilot applications.

Suggested Citation

  • Zuochun Fan & Mei Tian & Man Li & Yidi Mi & Yue Jiang & Tao Song & Jinxin Cao & Zheyu Liu, 2024. "Assessment of CO 2 Sequestration Capacity in a Low-Permeability Oil Reservoir Using Machine Learning Methods," Energies, MDPI, vol. 17(16), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:3979-:d:1454199
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/16/3979/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/16/3979/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    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. Xu, Zhongwen & Tan, Shiqi & Yao, Liming & Lv, Chengwei, 2024. "Exploring water-saving potentials of US electric power transition while thirsting for carbon neutrality," Energy, Elsevier, vol. 292(C).
    2. Meng, Qingwei & Sun, Hao & Fang, Fang, 2023. "Stochastic performance evaluation method of wind power DC bus voltage control system," Renewable Energy, Elsevier, vol. 219(P1).
    3. Zeng, Qingshun & Shi, Changfeng & Zhu, Wenjun & Zhi, Jiaqi & Na, Xiaohong, 2023. "Sequential data-driven carbon peaking path simulation research of the Yangtze River Delta urban agglomeration based on semantic mining and heuristic algorithm optimization," Energy, Elsevier, vol. 285(C).
    4. Qiangyi Li & Lan Yang & Shuang Huang & Yangqing Liu & Chenyang Guo, 2023. "The Effects of Urban Sprawl on Electricity Consumption: Empirical Evidence from 283 Prefecture-Level Cities in China," Land, MDPI, vol. 12(8), pages 1-27, August.
    5. Bo Yang & Yulin Li & Wei Yao & Lin Jiang & Chuanke Zhang & Chao Duan & Yaxing Ren, 2023. "Optimization and Control of New Power Systems under the Dual Carbon Goals: Key Issues, Advanced Techniques, and Perspectives," Energies, MDPI, vol. 16(9), pages 1-4, May.
    6. Di, Kaisheng & Chen, Weidong & Shi, Qiumei & Cai, Quanling & Liu, Sichen, 2024. "Analysing the impact of coupled domestic demand dynamics of green and low-carbon consumption in the market based on SEM-ANN," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    7. Lei Xu & Zhenzhen Lu & Zhiping Kang & Yingwen Duan & Junwei Zhang, 2023. "Carbon Emission Intensity and Its Abatement Choices: A Case of China Eastern," Sustainability, MDPI, vol. 15(23), pages 1-16, November.
    8. Chong Zhang & Ignacio Mauleón, 2023. "Assessing the Energy Efficiency Gains and Savings in China’s 2060 Carbon-Neutral Plan," Energies, MDPI, vol. 16(19), pages 1-16, September.

    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:jeners:v:17:y:2024:i:16:p:3979-:d:1454199. 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.