IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i6p1303-d1353710.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/6/1303/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/6/1303/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    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. Zhang, Kai & Lau, Hon Chung & Liu, Shuyang & Li, Hangyu, 2022. "Carbon capture and storage in the coastal region of China between Shanghai and Hainan," Energy, Elsevier, vol. 247(C).
    2. Al-Qahtani, Amjad & Parkinson, Brett & Hellgardt, Klaus & Shah, Nilay & Guillen-Gosalbez, Gonzalo, 2021. "Uncovering the true cost of hydrogen production routes using life cycle monetisation," Applied Energy, Elsevier, vol. 281(C).
    3. Nafees Ali & Xiaodong Fu & Umar Ashraf & Jian Chen & Hung Vo Thanh & Aqsa Anees & Muhammad Shahid Riaz & Misbah Fida & Muhammad Afaq Hussain & Sadam Hussain & Wakeel Hussain & Awais Ahmed, 2022. "Remote Sensing for Surface Coal Mining and Reclamation Monitoring in the Central Salt Range, Punjab, Pakistan," Sustainability, MDPI, vol. 14(16), pages 1-26, August.
    4. 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).
    5. Latifah M. Alsarhan & Alhanouf S. Alayyar & Naif B. Alqahtani & Nezar H. Khdary, 2021. "Circular Carbon Economy (CCE): A Way to Invest CO 2 and Protect the Environment, a Review," Sustainability, MDPI, vol. 13(21), pages 1-25, October.
    6. Norouzi, Amir Mohammad & Pouranian, Fatemeh & Rabbani, Arash & Fowler, Neil & Gluyas, Jon & Niasar, Vahid & Ezekiel, Justin & Babaei, Masoud, 2023. "CO2-plume geothermal: Power net generation from 3D fluvial aquifers," Applied Energy, Elsevier, vol. 332(C).
    7. Aliyon, Kasra & Rajaee, Fatemeh & Ritvanen, Jouni, 2023. "Use of artificial intelligence in reducing energy costs of a post-combustion carbon capture plant," Energy, Elsevier, vol. 278(PA).
    8. Fangshu Cui & Sheng Qin & Jing Zhang & Mengwei Li & Yuanhao Shi, 2022. "A Hybrid Method for Prediction of Ash Fouling on Heat Transfer Surfaces," Energies, MDPI, vol. 15(13), pages 1-15, June.
    9. Jiangyuan Yao & Wanju Yuan & Xiaolong Peng & Zhuoheng Chen & Yongan Gu, 2023. "A Novel Multi-Phase Strategy for Optimizing CO 2 Utilization and Storage in an Oil Reservoir," Energies, MDPI, vol. 16(14), pages 1-19, July.
    10. Bokka, Harsha Kumar & Lau, Hon Chung, 2023. "Decarbonising Vietnam's power and industry sectors by carbon capture and storage," Energy, Elsevier, vol. 262(PA).
    11. Marian Wiatowski & Krzysztof Kapusta & Kamil Stańczyk & Marcin Szyja & Shakil Masum & Sivachidambaram Sadasivam & Hywel Rhys Thomas, 2023. "Large-Scale Ex Situ Tests for CO 2 Storage in Coal Beds," Energies, MDPI, vol. 16(17), pages 1-14, August.
    12. 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).
    13. Vo Thanh, Hung & Zamanyad, Aiyoub & Safaei-Farouji, Majid & Ashraf, Umar & Hemeng, Zhang, 2022. "Application of hybrid artificial intelligent models to predict deliverability of underground natural gas storage sites," Renewable Energy, Elsevier, vol. 200(C), pages 169-184.

    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:6:p:1303-:d:1353710. 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.