IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v12y2025i4d10.1007_s40745-025-00619-7.html
   My bibliography  Save this article

Optimization of Oil and Gas Pipeline Leakage Data and Defect Identification Based on Graph Neural Processing

Author

Listed:
  • Lizhen Zhang

    (Chongqing Vocational Institute of Safety Technology)

Abstract

With the increasing complexity of oil and gas pipeline networks, early identification of leaks and defects is crucial to ensure the safe operation of pipelines. This study proposes a graph neural network (GNN) method for data processing and defect identification aimed at optimizing monitoring and maintenance strategies for oil and gas pipelines. Through the analysis of historical leakage data, we constructed a graph database containing 5000 samples, each containing 10 features such as pressure, flow, temperature, etc. Using graph convolutional network and graph attention network (GAT) to perform feature extraction and pattern recognition on nodes in pipeline network, our model achieves 92% accuracy in defect recognition, which is 15% higher than traditional methods. In addition, we have developed a leakage prediction model based on time series analysis, which is able to predict potential leakage risks 24 h in advance with an accuracy of 85%. The results of this study not only improve the safety management level of oil and gas pipelines, but also provide a new technical path for future intelligent pipeline maintenance.

Suggested Citation

  • Lizhen Zhang, 2025. "Optimization of Oil and Gas Pipeline Leakage Data and Defect Identification Based on Graph Neural Processing," Annals of Data Science, Springer, vol. 12(4), pages 1413-1430, August.
    • Handle: RePEc:spr:aodasc:v:12:y:2025:i:4:d:10.1007_s40745-025-00619-7
    DOI: 10.1007/s40745-025-00619-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-025-00619-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-025-00619-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:aodasc:v:12:y:2025:i:4:d:10.1007_s40745-025-00619-7. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.