IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i5p1078-d1597664.html
   My bibliography  Save this article

Research on Export Oil and Gas Concentration Prediction Based on Machine Learning Methods

Author

Listed:
  • Xiaochuan Wang

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Baikang Zhu

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Huajun Zheng

    (Zhejiang Oil Storage and Transportation Co., Ltd., Hangzhou 311227, China)

  • Jiaqi Wang

    (Zhejiang Oil Storage and Transportation Co., Ltd., Hangzhou 311227, China)

  • Zhiwei Chen

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Bingyuan Hong

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

Abstract

With the oil industry’s increasing focus on environmental protection and the growing implementation of oil and gas recovery devices in depots, it is crucial to investigate the outlet concentrations of oil and gas from these devices. This research aims to reduce energy consumption while enhancing the efficiency of oil and gas recovery processes. This paper investigates the prediction of outlet oil and gas concentration based on the process parameters of oil and gas recovery devices in oil depots. This study employs both regression and classification machine learning models. Most regression models achieve a goodness-of-fit of approximately 0.9 and an accuracy error of about 30%. Additionally, most classification models attain over 90% accuracy, with predictions of high oil and gas concentrations reaching up to 84.5% accuracy. Both models demonstrate that the Random Forest method is more effective in predicting the exported oil and gas concentration with multiple-parameter inputs, providing a relevant basis for subsequent control of exported oil and gas concentration.

Suggested Citation

  • Xiaochuan Wang & Baikang Zhu & Huajun Zheng & Jiaqi Wang & Zhiwei Chen & Bingyuan Hong, 2025. "Research on Export Oil and Gas Concentration Prediction Based on Machine Learning Methods," Energies, MDPI, vol. 18(5), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1078-:d:1597664
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/5/1078/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/5/1078/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kunming Tian & Zhihong Kang & Zhijiang Kang, 2024. "A Productivity Prediction Method of Fracture-Vuggy Reservoirs Based on the PSO-BP Neural Network," Energies, MDPI, vol. 17(14), pages 1-13, July.
    2. Alaa Ghanem & Mohammed F. Gouda & Rima D. Alharthy & Saad M. Desouky, 2022. "Predicting the Compressibility Factor of Natural Gas by Using Statistical Modeling and Neural Network," Energies, MDPI, vol. 15(5), pages 1-15, March.
    3. Cen, Xiao & Chen, Zengliang & Chen, Haifeng & Ding, Chen & Ding, Bo & Li, Fei & Lou, Fangwei & Zhu, Zhenyu & Zhang, Hongyu & Hong, Bingyuan, 2024. "User repurchase behavior prediction for integrated energy supply stations based on the user profiling method," Energy, Elsevier, vol. 286(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. Fu, Shuke & Ge, Yingchen & Hao, Yu & Peng, Jiachao & Tian, Jiali, 2024. "Energy supply chain efficiency in the digital era: Evidence from China's listed companies," Energy Economics, Elsevier, vol. 134(C).
    2. Yun Xia & Wenpeng Bai & Zhipeng Xiang & Wanbin Wang & Qiao Guo & Yang Wang & Shiqing Cheng, 2022. "Improvement of Gas Compressibility Factor and Bottom-Hole Pressure Calculation Method for HTHP Reservoirs: A Field Case in Junggar Basin, China," Energies, MDPI, vol. 15(22), pages 1-20, November.

    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:18:y:2025:i:5:p:1078-:d:1597664. 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.