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Corrosion induced failure analysis of subsea pipelines

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  • Yang, Yongsheng
  • Khan, Faisal
  • Thodi, Premkumar
  • Abbassi, Rouzbeh

Abstract

Pipeline corrosion is one of the main causes of subsea pipeline failure. It is necessary to monitor and analyze pipeline condition to effectively predict likely failure. This paper presents an approach to analyze the observed abnormal events to assess the condition of subsea pipelines. First, it focuses on establishing a systematic corrosion failure model by Bow-Tie (BT) analysis, and subsequently the BT model is mapped into a Bayesian Network (BN) model. The BN model facilitates the modelling of interdependency of identified corrosion causes, as well as the updating of failure probabilities depending on the arrival of new information. Furthermore, an Object-Oriented Bayesian Network (OOBN) has been developed to better structure the network and to provide an efficient updating algorithm. Based on this OOBN model, probability updating and probability adaptation are performed at regular intervals to estimate the failure probabilities due to corrosion and potential consequences. This results in an interval-based condition assessment of subsea pipeline subjected to corrosion. The estimated failure probabilities would help prioritize action to prevent and control failures. Practical application of the developed model is demonstrated using a case study.

Suggested Citation

  • Yang, Yongsheng & Khan, Faisal & Thodi, Premkumar & Abbassi, Rouzbeh, 2017. "Corrosion induced failure analysis of subsea pipelines," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 214-222.
    • Handle: RePEc:eee:reensy:v:159:y:2017:i:c:p:214-222
    DOI: 10.1016/j.ress.2016.11.014
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    3. Melchers, R.E. & Jeffrey, R.J., 2008. "Probabilistic models for steel corrosion loss and pitting of marine infrastructure," Reliability Engineering and System Safety, Elsevier, vol. 93(3), pages 423-432.
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    7. Dao, Uyen & Sajid, Zaman & Khan, Faisal & Zhang, Yahui & Tran, Trung, 2023. "Modeling and analysis of internal corrosion induced failure of oil and gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Hassan, Shamsu & Wang, Jin & Kontovas, Christos & Bashir, Musa, 2022. "An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
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    11. Chen, Xi & Bose, Neil & Brito, Mario & Khan, Faisal & Thanyamanta, Bo & Zou, Ting, 2021. "A Review of Risk Analysis Research for the Operations of Autonomous Underwater Vehicles," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. 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).
    13. Aalirezaei, Armin & Kabir, Dr. Golam & Khan, Md Saiful Arif, 2023. "Dynamic predictive analysis of the consequences of gas pipeline failures using a Bayesian network," International Journal of Critical Infrastructure Protection, Elsevier, vol. 43(C).
    14. Guo, Jian & Ma, Kaijiang, 2024. "Risk analysis for hazardous chemical vehicle-bridge transportation system: A dynamic Bayesian network model incorporating vehicle dynamics," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    15. Li, Xinhong & Jia, Ruichao & Zhang, Renren & Yang, Shangyu & Chen, Guoming, 2022. "A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    16. Adumene, Sidum & Khan, Faisal & Adedigba, Sunday & Zendehboudi, Sohrab & Shiri, Hodjat, 2021. "Dynamic risk analysis of marine and offshore systems suffering microbial induced stochastic degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    17. Hegde, Jeevith & Utne, Ingrid Bouwer & Schjølberg, Ingrid & Thorkildsen, Brede, 2018. "A Bayesian approach to risk modeling of autonomous subsea intervention operations," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 142-159.

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