Research on Risk Early Warning Model of Oil and Gas Pipeline Graph Neural Network Based on Knowledge Graph

Abstract

With the rapid expansion of oil and gas pipeline networks, their operational safety faces increasingly complex risk threats. Conventional accident risk assessment methods mainly rely on manually defined rules and static indicators, making it difficult to uncover the deeper causal logic and structural patterns of accidents. To address this challenge, a risk early-warning model for oil and gas pipelines is proposed, integrating knowledge graph techniques with graph neural networks. Specifically, pipeline accident reports from Pipeline and Hazardous Materials Safety Administration (PHMSA) are consolidated to construct a comprehensive knowledge graph of pipeline risks. Based on this graph, a relation-aware graph neural network node classification approach is designed, which incorporates both structural features and numerical attributes to enable risk prediction. Within this framework, the Composition-based Multi-Relational Graph Convolutional Networks (CompMRGCN) model is further developed, extending the relation-aware graph convolutional network by embedding a Markov random field-based dependency mechanism to capture correlations among node labels during prediction. Experimental results demonstrate that the proposed early-warning model and CompMRGCN method achieve 96.2% accuracy, 95.8% F1-score, and 97.1% mAP, representing improvements of 6.7%, 5.6%, and 5.4% over the best existing baselines, respectively. Comparative analysis indicates that this approach substantially outperforms competing models in terms of accuracy, generalization, and interpretability, offering an effective and practical technical support for intelligent accident early warning and safety management of oil and gas pipelines.