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<p>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&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月16日

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  • Lizhen Zhang

摘要

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.