Dynamic risk assessment of oil and gas pipeline failure based on DBN model

Chengzu Li; Gang Yao; Shengbao Hei; Yajun Wang; Xizeng Liu; Jia Guo; Lei Zhang

doi:10.1117/12.2692436

23 August 2023 Dynamic risk assessment of oil and gas pipeline failure based on DBN model

Chengzu Li, Gang Yao, Shengbao Hei, Yajun Wang, Xizeng Liu, Jia Guo, Lei Zhang

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 127840H (2023) https://doi.org/10.1117/12.2692436
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

In order to improve the accuracy of failure risk assessment of oil and gas pipelines, a dynamic analysis method based on bow tie-Bayesian network model was proposed. The bow tie model is used to classify the failure causes of oil and gas pipeline and the consequences of pipeline failure. The noise gate model and expert judgment are used to optimize the Bayesian network. The time dimension is introduced to construct the dynamic Bayes, and the susceptibility and severity of pipeline failure accidents are calculated by examples. The results show that the failure probability of the evaluation pipeline is 7.32×10^-3 times /(kmꞏa). In the posterior probability, the key factors of pipeline failure are soil migration, unclear pipeline labeling, corrosive substances in the medium and sand particles in the medium. The value of pipeline failure probability within 1~2 time steps is larger, and the risk is larger. In the accident consequence, the risk of explosion is greater than the risk of diffusion than the risk of no consequence, and the probability of accident consequence decreases with the increase of time step.

Citation Download Citation

Chengzu Li, Gang Yao, Shengbao Hei, Yajun Wang, Xizeng Liu, Jia Guo, and Lei Zhang "Dynamic risk assessment of oil and gas pipeline failure based on DBN model", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 127840H (23 August 2023); https://doi.org/10.1117/12.2692436

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