IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i7p2774-d1364982.html
   My bibliography  Save this article

Approaches of Combining Machine Learning with NMR-Based Pore Structure Characterization for Reservoir Evaluation

Author

Listed:
  • Wenjun Zhao

    (State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China)

  • Tangyan Liu

    (Faculty of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China)

  • Jian Yang

    (Engineering Technology Research Institute of Southwest Oil & Gas Field Company, PetroChina, Chengdu 610017, China)

  • Zhuo Zhang

    (State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China)

  • Cheng Feng

    (Faculty of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China)

  • Jizhou Tang

    (State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China)

Abstract

Tight gas, a category of unconventional natural gas, relies on advanced intelligent monitoring methods for their extraction. Conventional logging for reservoir evaluation relies on logging data and the manual setting of evaluation criteria to classify reservoirs. However, the complexity and heterogeneity of tight reservoirs pose challenges in accurately identifying target layers by using traditional well-logging techniques. Machine learning may hold the key to solving this problem, as it enables computers to learn without being explicitly programmed and manually adding rules. Therefore, it is possible to make reservoir evaluations using machine learning methods. In this paper, the reservoir quality index (RQI) and porous geometric parameters obtained from the optimized inversion of the spherical–tubular model are adopted to evaluate the reservoir. Then, three different machine learning approaches, the random forest (RF) algorithm, support vector machine (SVM) algorithm, and extreme gradient boosting (XGB) algorithm, are utilized for reservoir classification. The selected dataset covers more than 7000 samples from five wells. The data from four wells are arranged as the training dataset, and the data of the remaining one well is designed as the testing dataset to calculate the prediction accuracies of different machine learning algorithms. Among them, accuracies of RF, SVM, and XGB are all higher than 90%, and XGB owns the highest result by reaching 97%. Machine learning based approaches can greatly assist reservoir prediction by implementing the well-logging data. The research highlights the application of reservoir classification with a higher prediction accuracy by combining machine learning algorithms with NMR-logging-based pore structure characterization, which can provide a guideline for sweet spot identification within the tight formation. This not only optimizes resource extraction but also aligns with the global shift towards clean and renewable energy sources, promoting sustainability and reducing the carbon footprint associated with conventional energy production. In summary, the fusion of machine learning and NMR-logging-based reservoir evaluation plays a crucial role in advancing both energy efficiency and the transition to cleaner energy sources.

Suggested Citation

  • Wenjun Zhao & Tangyan Liu & Jian Yang & Zhuo Zhang & Cheng Feng & Jizhou Tang, 2024. "Approaches of Combining Machine Learning with NMR-Based Pore Structure Characterization for Reservoir Evaluation," Sustainability, MDPI, vol. 16(7), pages 1-23, March.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:2774-:d:1364982
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/7/2774/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/7/2774/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mao, Shaowen & Chen, Bailian & Malki, Mohamed & Chen, Fangxuan & Morales, Misael & Ma, Zhiwei & Mehana, Mohamed, 2024. "Efficient prediction of hydrogen storage performance in depleted gas reservoirs using machine learning," Applied Energy, Elsevier, vol. 361(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.

      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:jsusta:v:16:y:2024:i:7:p:2774-:d:1364982. 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.