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Prediction of ORF for Optimized CO 2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

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  • Ming Yue

    (State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 102206, China
    SINOPEC Key Laboratory of Carbon Capture, Utilization and Storage, Beijing 102206, China
    School of Civil and Resource Engineering, University of Science and Technology Beijing, No. 30, Xueyuan Road, Beijing 100083, China)

  • Quanqi Dai

    (State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 102206, China
    SINOPEC Key Laboratory of Carbon Capture, Utilization and Storage, Beijing 102206, China
    Petroleum Exploration and Development Research Institute, SINOPEC, Beijing 102206, China)

  • Haiying Liao

    (State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 102206, China
    SINOPEC Key Laboratory of Carbon Capture, Utilization and Storage, Beijing 102206, China
    Petroleum Exploration and Development Research Institute, SINOPEC, Beijing 102206, China)

  • Yunfeng Liu

    (State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 102206, China
    SINOPEC Key Laboratory of Carbon Capture, Utilization and Storage, Beijing 102206, China
    Petroleum Exploration and Development Research Institute, SINOPEC, Beijing 102206, China)

  • Lin Fan

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, No. 30, Xueyuan Road, Beijing 100083, China)

  • Tianru Song

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, No. 30, Xueyuan Road, Beijing 100083, China)

Abstract

Tight reservoirs characterized by complex physical properties pose significant challenges for extraction. CO 2 flooding, as an EOR technique, offers both economic and environmental advantages. Accurate prediction of recovery rate plays a crucial role in the development of tight oil and gas reservoirs. But the recovery rate is influenced by a complex array of factors. Traditional methods are time-consuming and costly and cannot predict the recovery rate quickly and accurately, necessitating advanced multi-factor analysis-based prediction models. This study uses machine learning models to rapidly predict the recovery of CO 2 flooding for tight oil reservoir development, establishes a numerical model for CO 2 flooding for low-permeability tight reservoir development based on actual blocks, studies the effects of reservoir parameters, horizontal well parameters, and injection-production parameters on CO 2 flooding recovery rate, and constructs a prediction model based on machine learning for the recovery. Using simulated datasets, three models, random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), were trained and tested for accuracy evaluation. Different levels of noise were added to the dataset and denoised, and the effects of data noise and denoising techniques on oil recovery factor prediction were studied. The results showed that the LightGBM model was superior to other models, with R 2 values of 0.995, 0.961, 0.921, and 0.877 for predicting EOR for the original dataset, 5% noise dataset, 10% noise dataset, and 15% noise dataset, respectively. Finally, based on the optimized model, the key control factors for CO 2 flooding for tight oil reservoirs to enhance oil recovery were analyzed. The novelty of this study is the development of a machine-learning-based method that can provide accurate and cost-effective ORF predictions for CO 2 flooding for tight oil reservoir development, optimize the development process in a timely manner, significantly reduce the required costs, and make it a more feasible carbon utilization and EOR strategy.

Suggested Citation

  • Ming Yue & Quanqi Dai & Haiying Liao & Yunfeng Liu & Lin Fan & Tianru Song, 2024. "Prediction of ORF for Optimized CO 2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning," Energies, MDPI, vol. 17(6), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1303-:d:1353710
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    References listed on IDEAS

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    1. Wenchao Liu & Yuejie Yang & Chengcheng Qiao & Chen Liu & Boyu Lian & Qingwang Yuan, 2023. "Progress of Seepage Law and Development Technologies for Shale Condensate Gas Reservoirs," Energies, MDPI, vol. 16(5), pages 1-30, March.
    2. Farajzadeh, R. & Eftekhari, A.A. & Dafnomilis, G. & Lake, L.W. & Bruining, J., 2020. "On the sustainability of CO2 storage through CO2 – Enhanced oil recovery," Applied Energy, Elsevier, vol. 261(C).
    3. Ting Chen & Laiming Song & Xueying Zhang & Yawen Yang & Huifang Fan & Bin Pan, 2023. "A Review of Mineral and Rock Wettability Changes Induced by Reaction: Implications for CO 2 Storage in Saline Reservoirs," Energies, MDPI, vol. 16(8), pages 1-17, April.
    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).
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