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A review of risk-based decision-making models for microbiologically influenced corrosion (MIC) in offshore pipelines

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  • Yazdi, Mohammad
  • Khan, Faisal
  • Abbassi, Rouzbeh
  • Quddus, Noor
  • Castaneda-Lopez, Homero

Abstract

Microbiologically influenced corrosion (MIC) is one of the critical integrity threats in marine and offshore industrial sectors. Thus, MIC should be considered for effective risk-based decision-making and asset integrity management of systems. The experience with accidents in this domain indicates that many corroded subsea pipelines involve a complex failure mode with MIC implications. Researchers have actively studied the MIC characteristics, mechanisms, modeling, and management since the last decades. However, despite MIC importance and practical implications for a better understanding of decision-makers, there is a lack of reliable knowledge of risk-based decision-making models for MIC in marine and offshore sectors. The current work aims to present a systematic attempt to identify the gaps, needs, and challenges of MIC in risk-based decision-making models. Therefore, an analysis of the arts in different database core collections is conducted. The analysis is focused on MIC characteristics, mechanisms, modeling, and management. It integrates the empirical and theoretical conclusions, highlighting the capabilities and drawbacks of existing literature and explaining the further research tasks’ opportunities.

Suggested Citation

  • Yazdi, Mohammad & Khan, Faisal & Abbassi, Rouzbeh & Quddus, Noor & Castaneda-Lopez, Homero, 2022. "A review of risk-based decision-making models for microbiologically influenced corrosion (MIC) in offshore pipelines," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:reensy:v:223:y:2022:i:c:s0951832022001363
    DOI: 10.1016/j.ress.2022.108474
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    Cited by:

    1. Woloszyk, Krzysztof & Garbatov, Yordan, 2024. "A probabilistic-driven framework for enhanced corrosion estimation of ship structural components," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    2. Yanyan Liu & Keping Li & Dongyang Yan & Shuang Gu, 2023. "The prediction of disaster risk paths based on IECNN model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 163-188, May.
    3. Ji, Ziguang & Chen, Yi & Ma, Xiaobing & Cai, Yikun & Yang, Li, 2024. "Hierarchical condition-based maintenance planning for corrosion process considering natural environmental impact," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    4. Wang, Mengmeng & Incecik, Atilla & Feng, Shizhe & Gupta, M.K. & Królczyk, Grzegorz & Li, Z, 2023. "Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    5. Amaya-Gómez, Rafael & Sánchez-Silva, Mauricio & Muñoz, Felipe & Schoefs, Franck & Bastidas-Arteaga, Emilio, 2024. "Spatial characterization and simulation of new defects in corroded pipeline based on In-Line Inspections," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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