Quantitative Risk Analysis of Offshore Fire and Explosion Based on the Analysis of Human and Organizational Factors

Wang, Yan Fu; Li, Yu Lian; Zhang, Biao; Pei, Yan; Zhang, Li

doi:10.1155/2015/537362

Cited by 11 publications

(12 citation statements)

References 29 publications

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“…Due to high cost of offshore processing plant, severity of fire and/or explosion, and complexity of marine environment, it becomes an imperative necessity to determine fire and/or explosion risk analysis . As stated by Wang et al , three kinds of researches are carried out worldwide on offshore fire and/or explosion risk analysis which comprises; (1) statistical methods, (2) use of commercial software, and (3) integration of new theory with traditional risk assessment methods. Among these methods, statistical methods are used to predict fire and/or explosion risk based on historical data .…”

Section: Brief Review Of Risk Assessment Methodologiesmentioning

confidence: 99%

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

Baksh

Garaniya

Khan

2016

Process Safety Progress

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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 causeconsequences 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.

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Section: Brief Review Of Risk Assessment Methodologiesmentioning

confidence: 99%

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

Baksh

Garaniya

Khan

2016

Process Safety Progress

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“…The ISO 45001 takes into account other international standards, like Occupational Health and Safety Assessment Series Overall, HSEMS is a tool based on risk management that ensures HSE policy and principles are applied consistently to offshore operations. By collecting human and organizational data, a mature HSEMS conducts quantitative risk assessment (QRA) to help operators evaluate safety of offshore operations and optimize production [35]. The QAR has been fully adopted by petroleum companies in countries with high HSE level such as Norway and U.K., while it lacks systematical solution and research tools in Chinese HSE management practice [36].…”

Section: Regulating Hse Management System For Offshore Operationsmentioning

confidence: 99%

Reforming Health, Safety, and Environmental Regulation for Offshore Operations in China: Risk and Resilience Approaches?

Yuan

2019

Sustainability

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Offshore drilling accidents have triggered regulatory reforms in China. The reforms aim to explore proper regulatory approaches to supervise offshore operations and improve their health, safety and environmental (HSE) performance. This study offers a review on the roles of risk and resilience in managing offshore operations and a well-defined analysis on their integrations with Chinese laws and regulations. The study finds risk and resilience approaches can promote the effectiveness of HSE regulation for offshore operations, while both are difficult to be transposed into legally binding rules in China. To fully develop and implement risk regulation for offshore operations, the study suggests to decentralize China’s command-and-control regulatory regime and encourage self-regulation in offshore petroleum companies. Transposing resilience thinking into legal practice is also highlighted so that various regulatory powers can keep proactive and flexible to any possible changes and uncertainties.

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“…Among the methods used to carry out qualitative and quantitative analysis of evacuation on offshore platforms, Bayesian Network (BN) has been attracting particular attentions [8,[18][19][20][21] because of its backward diagnosis and forward prediction analysis ability [22] . Usually, BN is combined with other methods, such as HOFs [23] , Reason's "Swiss cheese" model [24] , Binomial distribution [25] , Human Reliability Analysis [26] , or Analytic Hierarchy Process to satisfy different purposes. However, there has been no well-known approach for dealing with expert judgment [27] .…”

Section: Introductionmentioning

confidence: 99%

Reliabilities analysis of evacuation on offshore platforms: A dynamic Bayesian Network model

Yan

Wang

et al. 2021

Process Safety and Environmental Protection

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An offshore platform is naturally vulnerable to accidents, such as the leakage of dangerous chemicals, fire and explosion because there are a lot of oil and gas, where all the equipment and pipes are squeezed into a limited area. Escape, Evacuation, and Rescue (EER) plans play a vital role as the last barrier to ensure the safety of personnel in the event of a major accident. As a result, the main contributors leading to evacuation failure are analyzed in this study to prioritize technology development needed to select a robust EER strategy. The scope of this research focuses on the quantitative analysis of various EER strategies on offshore platforms. In this research, a reliability prediction model of emergency evacuation is established for offshore platforms based on the K2 structure learning algorithm and a Bayesian network parameter learning method. The conditional probability tables of each node are determined by combining the Bayesian estimation method and a junction tree reasoning engine. The reliability of emergency evacuation on a platform is predicted using a dynamic Bayesian network model. The transition probability is determined through a Markov method. The main factors leading to evacuation failure are investigated using the diagnostic reasoning method of Bayesian Network.

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Quantitative Risk Analysis of Offshore Fire and Explosion Based on the Analysis of Human and Organizational Factors

Cited by 11 publications

References 29 publications

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

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

Reforming Health, Safety, and Environmental Regulation for Offshore Operations in China: Risk and Resilience Approaches?

Reliabilities analysis of evacuation on offshore platforms: A dynamic Bayesian Network model

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