A network based approach to envisage potential accidents in offshore process facilities

Authors

  • Al-Amin Baksh,

    1. National Centre of Maritime Engineering and Hydrodynamics (NCMEH), Australian Maritime College (AMC) University of Tasmania, Launceston, TAS, Australia
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  • Rouzbeh Abbassi,

    1. National Centre of Maritime Engineering and Hydrodynamics (NCMEH), Australian Maritime College (AMC) University of Tasmania, Launceston, TAS, Australia
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  • Vikram Garaniya,

    1. National Centre of Maritime Engineering and Hydrodynamics (NCMEH), Australian Maritime College (AMC) University of Tasmania, Launceston, TAS, Australia
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  • Faisal Khan

    Corresponding author
    1. National Centre of Maritime Engineering and Hydrodynamics (NCMEH), Australian Maritime College (AMC) University of Tasmania, Launceston, TAS, Australia
    2. Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada
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  • This article was published online on 28 September 2016. An error was subsequently identified. This notice is included in the online and print versions to indicate that both have been corrected on 10 November 2016.

  • This work was supported by National Centre for Maritime Engineering and Hydrodynamics (NCMEH) of the Australian Maritime College (AMC) at the University of Tasmania.

Abstract

Envisaging potential accidents in large scale offshore process facilities such as Floating Liquefied Natural Gas (FLNG) is complex and could be best characterized through evolving scenarios. In the present work, a new methodology is developed to incorporate evolving scenarios in a single model and predicts the likelihood of accident. The methodology comprises; (a) evolving scenario identification, (b) accident consequence framework development, (c) accident scenario likelihood estimation, and (d) ranking of the scenarios. Resulting events in the present work are modeled using a Bayesian network approach, which represents accident scenarios as cause-consequences networks. The methodology developed in this article is compared with case studies of ammonia and Liquefied Natural Gas from chemical and offshore process facility, respectively. The proposed method is able to differentiate the consequence of specific events and predict probabilities for such events along with continual updating of consequence probabilities of fire and explosion scenarios taking into account. The developed methodology can be used to envisage evolving scenarios that occur in the offshore oil and gas process industry; however, with further modification it can be applied to different sections of marine industry to predict the likelihood of such accidents. © 2016 American Institute of Chemical Engineers Process Saf Prog 36: 178–191, 2017

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