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Dynamic occupational risk model for offshore operations in harsh environments

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  • Song, Guozheng
  • Khan, Faisal
  • Wang, Hangzhou
  • Leighton, Shelly
  • Yuan, Zhi
  • Liu, Hanwen

Abstract

The expansion of offshore oil exploitation into remote areas (e.g., Arctic) with harsh environments has significantly increased occupational risks. Among occupational accidents, slips, trips and falls from height (STFs) account for a significant portion. Thus, a dynamic risk assessment of the three main occupational accidents is meaningful to decrease offshore occupational risks. Bow-tie Models (BTs) were established in this study for the risk analysis of STFs considering extreme environmental factors. To relax the limitations of BTs, Bayesian networks (BNs) were developed based on BTs to dynamically assess risks of STFs. The occurrence and consequence probabilities of STFs were respectively calculated using BTs and BNs, and the obtained probabilities verified BNs׳ rationality and advantage. Furthermore, the probability adaptation for STFs was accomplished in a specific scenario with BNs. Finally, posterior probabilities of basic events were achieved through diagnostic analysis, and critical basic events were analyzed based on their posterior likelihood to cause occupational accidents. The highlight is systematically analyzing STF accidents for offshore operations and dynamically assessing their risks considering the harsh environmental factors. This study can guide the allocation of prevention resources and benefit the safety management of offshore operations.

Suggested Citation

  • Song, Guozheng & Khan, Faisal & Wang, Hangzhou & Leighton, Shelly & Yuan, Zhi & Liu, Hanwen, 2016. "Dynamic occupational risk model for offshore operations in harsh environments," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 58-64.
    • Handle: RePEc:eee:reensy:v:150:y:2016:i:c:p:58-64
    DOI: 10.1016/j.ress.2016.01.021
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    References listed on IDEAS

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    1. Carmen Carnero, María & José Pedregal, Diego, 2010. "Modelling and forecasting occupational accidents of different severity levels in Spain," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1134-1141.
    2. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2013. "Risk-based design of process systems using discrete-time Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 5-17.
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    Cited by:

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    4. Papazoglou, I.A. & Aneziris, O.N. & Bellamy, L.J. & Ale, B.J.M. & Oh, J., 2017. "Quantitative occupational risk model: Single hazard," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 162-173.
    5. Qiu, Na & Liu, Xiuquan & Li, Yanwei & Hu, Pengji & Chang, Yuanjiang & Chen, Guoming & Meng, Huixing, 2024. "Dynamic catastrophe analysis of deepwater mooring platform/riser/wellhead coupled system under ISW," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
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    7. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C., 2022. "Casualty analysis methodology and taxonomy for FPSO accident analysis," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    8. Leimeister, Mareike & Kolios, Athanasios, 2018. "A review of reliability-based methods for risk analysis and their application in the offshore wind industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1065-1076.
    9. 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).
    10. 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).

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