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

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  • 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
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    References listed on IDEAS

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    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).
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