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Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas

Author

Listed:
  • Yin, Yuanbo
  • Yang, Hao
  • Duan, Pengfei
  • Li, Luling
  • Zio, Enrico
  • Liu, Cuiwei
  • Li, Yuxing

Abstract

Currently, quantitative risk assessment is often used for pipeline safety analysis. However, the low probability of pipeline failure may produce a false safety evaluation result because high-consequence areas with high population densities cannot be effectively identified. Furthermore, nearly half of the gas leakage accidents in China have occurred in densely populated areas. Therefore, an improved quantitative risk assessment method is proposed. First, we establish two models: (1) a failure probability model based on improved historical failure and disaster derivation probabilities and (2) a risk consequence model considering potential direct and indirect losses based on the probability of disaster evolution. Considering the concept of human rights equality and the social model of “life is first†in China, a method to correct the loss value of life according to population density is proposed, which can effectively avoid the hidden phenomenon of high-consequence areas mentioned above. A pipeline in China is evaluated using this improved method. Compared with traditional evaluation results, the new evaluation method can effectively identify a high-consequence area and obtain more reasonable results. Thus, a pipeline maintenance plan can ensure the interests of enterprises and fully respect the lives of individuals threatened by the potential risk of the pipeline.

Suggested Citation

  • Yin, Yuanbo & Yang, Hao & Duan, Pengfei & Li, Luling & Zio, Enrico & Liu, Cuiwei & Li, Yuxing, 2022. "Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002290
    DOI: 10.1016/j.ress.2022.108583
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    as
    1. Su, Yue & Li, Jingfa & Yu, Bo & Zhao, Yanlin & Yao, Jun, 2021. "Fast and accurate prediction of failure pressure of oil and gas defective pipelines using the deep learning model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Yu, Xuchao & Liang, Wei & Zhang, Laibin & Reniers, Genserik & Lu, Linlin, 2018. "Risk assessment of the maintenance process for onshore oil and gas transmission pipelines under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 50-67.
    3. Liu, Aihua & Chen, Ke & Huang, Xiaofei & Li, Didi & Zhang, Xiaochun, 2021. "Dynamic risk assessment model of buried gas pipelines based on system dynamics," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    4. Brito, A.J. & de Almeida, A.T., 2009. "Multi-attribute risk assessment for risk ranking of natural gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 187-198.
    5. Zhang, Hewei & Dong, Shaohua & Ling, Jiatong & Zhang, Laibin & Cheang, Brenda, 2020. "A modified method for the safety factor parameter: The use of big data to improve petroleum pipeline reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    6. Syed, Zaki & Lawryshyn, Yuri, 2020. "Risk analysis of an underground gas storage facility using a physics-based system performance model and Monte Carlo simulation," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    7. Yu, Weichao & Huang, Weihe & Wen, Kai & Zhang, Jie & Liu, Hongfei & Wang, Kun & Gong, Jing & Qu, Chunxu, 2021. "Subset simulation-based reliability analysis of the corroding natural gas pipeline," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    8. Su, Huai & Zio, Enrico & Zhang, Jinjun & Li, Xueyi, 2018. "A systematic framework of vulnerability analysis of a natural gas pipeline network," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 79-91.
    9. Dundulis, Gintautas & ŽutautaitÄ—, Inga & Janulionis, Remigijus & UÅ¡puras, Eugenijus & RimkeviÄ ius, Sigitas & Eid, Mohamed, 2016. "Integrated failure probability estimation based on structural integrity analysis and failure data: Natural gas pipeline case," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 195-202.
    10. Liu, Cuiwei & Wang, Yazhen & Li, Xinhong & Li, Yuxing & Khan, Faisal & Cai, Baoping, 2021. "Quantitative assessment of leakage orifices within gas pipelines using a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    11. Medeiros, Cristina Pereira & da Silva, Lucas Borges Leal & Alencar, Marcelo Hazin & de Almeida, Adiel Teixeira, 2021. "A new method for managing multidimensional risks in Natural Gas Pipelines based on non-Expected Utility," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    12. Medeiros, C.P. & Alencar, M.H. & de Almeida, A.T., 2017. "Multidimensional risk evaluation of natural gas pipelines based on a multicriteria decision model using visualization tools and statistical tests for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 268-276.
    13. Russo, Paola & Parisi, Fulvio, 2016. "Risk-targeted safety distance of reinforced concrete buildings from natural-gas transmission pipelines," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 57-66.
    14. Wu, Wei-Shing & Yang, Chen-Feng & Chang, Jung-Chuan & Château, Pierre-Alexandre & Chang, Yang-Chi, 2015. "Risk assessment by integrating interpretive structural modeling and Bayesian network, case of offshore pipeline project," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 515-524.
    15. Xiang, W. & Zhou, W., 2021. "Bayesian network model for predicting probability of third-party damage to underground pipelines and learning model parameters from incomplete datasets," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    16. Dimaio, F. & Scapinello, O. & Zio, E. & Ciarapica, C. & Cincotta, S. & Crivellari, A. & Decarli, L. & Larosa, L., 2021. "Accounting for Safety Barriers Degradation in the Risk Assessment of Oil and Gas Systems by Multistate Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    17. Heidary, Roohollah & Groth, Katrina M., 2021. "A hybrid population-based degradation model for pipeline pitting corrosion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
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    1. Wu, Xingguang & Huang, Huirong & Xie, Jianyu & Lu, Meixing & Wang, Shaobo & Li, Wang & Huang, Yixuan & Yu, Weichao & Sun, Xiaobo, 2023. "A novel dynamic risk assessment method for the petrochemical industry using bow-tie analysis and Bayesian network analysis method based on the methodological framework of ARAMIS project," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Zhang, Tieyao & Shuai, Jian & Shuai, Yi & Hua, Luoyi & Xu, Kui & Xie, Dong & Mei, Yuan, 2023. "Efficient prediction method of triple failure pressure for corroded pipelines under complex loads based on a backpropagation neural network," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Xiao, Rui & Zayed, Tarek & Meguid, Mohamed A. & Sushama, Laxmi, 2024. "Improving failure modeling for gas transmission pipelines: A survival analysis and machine learning integrated approach," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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