Multistate Probabilistic Assessment of Third-Party Damage Risk for Oil and Gas Pipelines Based on DEMATEL-ISM-Røed-BN
Publication: Journal of Performance of Constructed Facilities
Volume 38, Issue 4
Abstract
As the lifeblood of national energy, oil and gas pipelines are vulnerable to corrosion, third-party damage, and natural disasters. Among these, third-party damage accidents have a high probability and serious consequences. However, China lacks a mature and unified pipeline accident database, making it difficult to assess and manage the risk of such incidents. To address this issue, this paper identifies 20 key risk factors for third-party damage to oil and gas pipelines through literature and case studies. The paper then proposes a multistate probabilistic assessment model using the Decision-Making Trial and Evaluation Laboratory (DEMATEL), interpretative structural modeling method (ISM), Røed method, and Bayesian network (BN). Real case studies demonstrate that the model not only predicts the probability of third-party damage risks but also clearly identifies key influencing factors and causal chains in the risk system. This provides a new approach for assessing third-party damage risks to oil and gas pipelines and offers a scientific basis for decision-making in managing actual pipelines.
Practical Applications
The high probability and serious consequences of third-party damage accidents of oil and gas pipelines have caused certain hidden dangers to social security, environment, and economy, and put forward higher requirements for pipeline operation and management. In order to scientifically manage and effectively control the occurrence of third-party damage accidents of oil and gas pipelines, this paper, in the absence of an applicable pipeline failure database. Based on expert knowledge, a model that can be used for the assessment and prediction of third-party damage probability of oil and gas pipelines is constructed. Through the application of real cases, it is shown that the model can be used not only to predict the probability of third-party damage of oil and gas pipelines, but also to identify the key influencing factors and key causal chains leading to third-party damage. The results of the study not only provide theoretical support for pipeline management in reality, but also provide new ideas for risk assessment of oil and gas pipelines that lack actual data.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study was supported by the project (Grant No. 23SYSX0146) of the Science and Technology Department of Sichuan Province.
References
Ahmadi, O., S. B. Mortazavi, H. A. Mahabadi, and M. Hosseinpouri. 2020. “Development of a dynamic quantitative risk assessment methodology using fuzzy DEMATEL-BN and leading indicators.” Process Saf. Environ. Prot. 142 (Oct): 15–44. https://doi.org/10.1016/j.psep.2020.04.038.
Aulia, R., H. Tan, and S. Sriramula. 2021. “Dynamic reliability model for subsea pipeline risk assessment due to third-party interference.” J. Pipeline Sci. Eng. 1 (3): 277–289. https://doi.org/10.1016/j.jpse.2021.09.006.
Badida, P., Y. Balasubramaniam, and J. Jayaprakash. 2019. “Risk evaluation of oil and natural gas pipelines due to natural hazards using fuzzy fault tree analysis.” J. Nat. Gas Sci. Eng. 66 (Jun): 284–292. https://doi.org/10.1016/j.jngse.2019.04.010.
Bajcar, T., F. Cimerman, and B. Širok. 2015. “Towards more detailed determination of third party impact on risk on natural gas pipelines: Influence of population density.” Process Saf. Environ. Prot. 94 (Mar): 509–516. https://doi.org/10.1016/j.psep.2014.11.001.
Bonvicini, S., G. Antonioni, and V. Cozzani. 2018. “Assessment of the risk related to environmental damage following major accidents in onshore pipelines.” J. Loss Prev. Process Ind. 56 (Nov): 505–516. https://doi.org/10.1016/j.jlp.2018.11.005.
Bubbico, R., S. Lee, D. Moscati, and N. Paltrinieri. 2020. “Dynamic assessment of safety barriers preventing escalation in offshore Oil&Gas.” Saf. Sci. 121 (Jan): 319–330. https://doi.org/10.1016/j.ssci.2019.09.011.
Chen, H., S. Liu, X. Wanyan, L. Pang, Y. Dang, K. Zhu, and X. Yu. 2023a. “Influencing factors of novice pilot SA based on DEMATEL-AISM method: From pilots’ view.” Heliyon 9 (2): e13425. https://doi.org/10.1016/j.heliyon.2023.e13425.
Chen, X., Z. Wu, W. Chen, R. Kang, X. He, and Y. Miao. 2019. “Selection of key indicators for reputation loss in oil and gas pipeline failure event.” Eng. Fail. Anal. 99 (Mar): 69–84. https://doi.org/10.1016/j.engfailanal.2019.01.071.
Chen, X.-L., W.-D. Lin, C.-X. Liu, F.-Q. Yang, Y. Guo, X. Li, S.-Q. Yuan, and G. Reniers. 2023b. “An integrated EDIB model for probabilistic risk analysis of natural gas pipeline leakage accidents.” J. Loss Prev. Process Ind. 83 (Jul): 105027. https://doi.org/10.1016/j.jlp.2023.105027.
Cui, Y., N. Quddus, and C. V. Mashuga. 2020. “Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines.” Process Saf. Environ. Prot. 134 (Feb): 178–188. https://doi.org/10.1016/j.psep.2019.11.038.
Das, B. 2004. “Generating conditional probabilities for Bayesian networks: Easing the knowledge acquisition problem.” Preprint, submitted November 12, 2004. http://arxiv.org/abs/0411034.
EGIG (European Gas Pipeline Incident Data Group). 2020. “11th report of the European gas pipeline incident data group (period 1970–2019) comprising.” Accessed March 17, 2023. https://www.EGIG.eu.
Fan, X., D. Wang, Z. Tong, and X. Wang. 2023. “Investigation and analysis of the safety risk factors of aging construction workers.” Saf. Sci. 167 (Nov): 106281. https://doi.org/10.1016/j.ssci.2023.106281.
Göksu, B., O. Yüksel, and C. Şakar. 2023. “Risk assessment of the Ship steering gear failures using fuzzy-Bayesian networks.” Ocean Eng. 274 (Apr): 114064. https://doi.org/10.1016/j.oceaneng.2023.114064.
Guo, X., L. Zhang, W. Liang, and S. Haugen. 2018. “Risk identification of third-party damage on oil and gas pipelines through the Bayesian network.” J. Loss Prev. Process Ind. 54 (Jul): 163–178. https://doi.org/10.1016/j.jlp.2018.03.012.
Guo, Y., X. Meng, D. Wang, T. Meng, S. Liu, and R. He. 2016. “Comprehensive risk evaluation of long-distance oil and gas transportation pipelines using a fuzzy Petri net model.” J. Nat. Gas Sci. Eng. 33 (Jul): 18–29. https://doi.org/10.1016/j.jngse.2016.04.052.
Hassan, S., J. Wang, C. Kontovas, and M. Bashir. 2022. “An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using Bayesian networks.” Reliab. Eng. Syst. Saf. 218 (Feb): 108171. https://doi.org/10.1016/j.ress.2021.108171.
Hong, B., B. Shao, J. Guo, J. Fu, C. Li, and B. Zhu. 2023. “Dynamic Bayesian network risk probability evolution for third-party damage of natural gas pipelines.” Appl. Energy 333 (Mar): 120620. https://doi.org/10.1016/j.apenergy.2022.120620.
Hu, Y., K. Liu, D. Xu, Z. Zhai, and H. Liu. 2017. “Risk assessment of long distance oil and gas pipeline based on grey clustering.” In Proc., IEEE Int. Conf. on Big Knowledge (ICBK), 198–201. New York: IEEE.
Kabir, S., and Y. Papadopoulos. 2019. “Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review.” Saf. Sci. 115 (Jun): 154–175. https://doi.org/10.1016/j.ssci.2019.02.009.
Kaplan, S. 2000. “Combining probability distributions from experts in risk analysis.” Risk Anal. 20 (2): 155–156. https://doi.org/10.1111/0272-4332.202015.
Khalilzadeh, M., H. Shakeri, and S. Zohrehvandi. 2021. “Risk identification and assessment with the fuzzy DEMATEL-ANP method in oil and gas projects under uncertainty.” Procedia Comput. Sci. 181 (Jan): 277–284. https://doi.org/10.1016/j.procs.2021.01.147.
Kraidi, L., R. Shah, W. Matipa, and F. Borthwick. 2019. “Analyzing the critical risk factors associated with oil and gas pipeline projects in Iraq.” Int. J. Crit. Infrastruct. Prot. 24 (Mar): 14–22. https://doi.org/10.1016/j.ijcip.2018.10.010.
Lemmer, J. F., and D. E. Gossink. 2004. “Recursive noisy OR-a rule for estimating complex probabilistic interactions.” IEEE Trans. Syst. Man. Cybern. Part B Cybern. 34 (6): 2252–2261. https://doi.org/10.1109/TSMCB.2004.834424.
Li, F., W. Wang, S. Dubljevic, F. Khan, J. Xu, and J. Yi. 2019a. “Analysis on accident-causing factors of urban buried gas pipeline network by combining DEMATEL, ISM and BN methods.” J. Loss Prev. Process Ind. 61 (Sep): 49–57. https://doi.org/10.1016/j.jlp.2019.06.001.
Li, P., X. Jin, Y. Wang, L. Dai, and Z. Luo. 2021a. “Reliability evaluation method of team situation awareness in digital nuclear power plant.” At. Energy Sci. Technol. 55 (1): 159–168. https://doi.org/10.7538/yzk.2020.youxian.0023.
Li, X., G. Chen, S. Jiang, R. He, C. Xu, and H. Zhu. 2018. “Developing a dynamic model for risk analysis under uncertainty: Case of third-party damage on subsea pipelines.” J. Loss Prev. Process Ind. 54 (Jul): 289–302. https://doi.org/10.1016/j.jlp.2018.05.001.
Li, X., Z. Han, C. Lu, and J. Zhao. 2020a. “Research on failure risk evaluation methodology of aging urban oil and gas pipeline.” China Saf. Sci. J. 30 (2): 93–98. https://doi.org/10.16265/j.cnki.issn1003-3033.2020.02.015.
Li, X., M. Yang, and G. Chen. 2019b. “An integrated framework for subsea pipelines safety analysis considering causation dependencies.” Ocean Eng. 183 (Jul): 175–186. https://doi.org/10.1016/j.oceaneng.2019.04.064.
Li, X., Y. Zhang, R. Abbassi, M. Yang, R. Zhang, and G. Chen. 2021b. “Dynamic probability assessment of urban natural gas pipeline accidents considering integrated external activities.” J. Loss Prev. Process Ind. 69 (Mar): 104388. https://doi.org/10.1016/j.jlp.2020.104388.
Li, Y., D. Xu, and J. Shuai. 2020b. “Real-time risk analysis of road tanker containing flammable liquid based on fuzzy Bayesian network.” Process Saf. Environ. Prot. 134 (Feb): 36–46. https://doi.org/10.1016/j.psep.2019.11.033.
Liang, W., J. Hu, L. Zhang, C. Guo, and W. Lin. 2012. “Assessing and classifying risk of pipeline third-party interference based on fault tree and SOM.” Eng. Appl. Artif. Intell. 25 (3): 594–608. https://doi.org/10.1016/j.engappai.2011.08.010.
Mkrtchyan, L., L. Podofillini, and V. N. Dang. 2016. “Methods for building conditional probability tables of Bayesian belief networks from limited judgment: An evaluation for human reliability application.” Reliab. Eng. Syst. Saf. 151 (Jul): 93–112. https://doi.org/10.1016/j.ress.2016.01.004.
Mohammadi, H., F. Laal, F. Mohammadian, P. Yari, M. Kangavari, and S. Moradi Hanifi. 2023. “Dynamic risk assessment of storage tank using consequence modeling and fuzzy Bayesian network.” Heliyon 9 (8): e18842. https://doi.org/10.1016/j.heliyon.2023.e18842.
PHMSA (United States Pipeline and Hazardous Materials Safety Administration). 2023. “Serious incident rate and cause.” Accessed March 20, 2023. https://www.phmsa.dot.gov/data-and-statistics/pipeline/national-pipeline-performance-measures/.
Raveendran, A., V. R. Renjith, and G. Madhu. 2022. “A comprehensive review on dynamic risk analysis methodologies.” J. Loss Prev. Process Ind. 76 (May): 104734. https://doi.org/10.1016/j.jlp.2022.104734.
Røed, W., A. Mosleh, J. E. Vinnem, and T. Aven. 2009. “On the use of the hybrid causal logic method in offshore risk analysis.” Reliab. Eng. Syst. Saf. 94 (2): 445–455. https://doi.org/10.1016/j.ress.2008.04.003.
Wang, X., and Q. Duan. 2018. “Establishment of risk system of oil and gas pipelines based on barrier model and FTA.” Saf. Environ. Eng. 163 (6): 76–83. https://doi.org/10.1016/j.ecoenv.2018.07.063.
Woldesellasse, H., and S. Tesfamariam. 2023. “Consequence assessment of gas pipeline failure caused by external pitting corrosion using an integrated Bayesian belief network and GIS model: Application with Alberta pipeline.” Reliab. Eng. Syst. Saf. 240 (Dec): 109573. https://doi.org/10.1016/j.ress.2023.109573.
Xiang, W., and W. Zhou. 2021. “Bayesian network model for predicting probability of third-party damage to underground pipelines and learning model parameters from incomplete datasets.” Reliab. Eng. Syst. Saf. 205 (Jan): 107262. https://doi.org/10.1016/j.ress.2020.107262.
Zarei, E., N. Khakzad, V. Cozzani, and G. Reniers. 2019. “Safety analysis of process systems using fuzzy Bayesian network (FBN).” J. Loss Prev. Process Ind. 57 (Jan): 7–16. https://doi.org/10.1016/j.jlp.2018.10.011.
Zhao, T., W. Zhou, K. Xu, and L. He. 2019. “Analysis and Bayesian modeling of tower crane safety risk during the use phase.” Sci. Tech. Eng. 19 (11): 350–356. https://doi.org/10.3969/j.issn.1671-1815.2019.11.054.
Zhou, Q.-Y., B. Li, Y. Lu, J. Chen, C.-M. Shu, and M.-S. Bi. 2023. “Dynamic risk analysis of oil depot storage tank failure using a fuzzy Bayesian network model.” Process Saf. Environ. Prot. 173 (May): 800–811. https://doi.org/10.1016/j.psep.2023.03.072.
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Published In
Journal of Performance of Constructed Facilities
Volume 38 • Issue 4 • August 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Oct 17, 2023
Accepted: Mar 5, 2024
Published online: May 30, 2024
Published in print: Aug 1, 2024
Discussion open until: Oct 30, 2024
ASCE Technical Topics:
- Accidents
- Business management
- Case studies
- Disaster risk management
- Energy infrastructure
- Engineering fundamentals
- Gas pipelines
- Infrastructure
- Lifeline systems
- Mathematics
- Methodology (by type)
- Models (by type)
- Oil pipelines
- Practice and Profession
- Probability
- Public administration
- Public health and safety
- Research methods (by type)
- Risk management
- Structural models
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