Centre for Offshore Engineering and Safety Technology (COEST), China University of Petroleum (East China), Qingdao 266580, China.
Navigation College, Dalian Maritime University, Dalian 116026, China.
Int J Environ Res Public Health. 2022 Jun 13;19(12):7216. doi: 10.3390/ijerph19127216.
Storm disasters are the most common cause of accidents in offshore oil and gas industries. To prevent accidents resulting from storms, it is vital to analyze accident propagation and to learn about accident mechanism from previous accidents. In this paper, a novel risk analysis framework is proposed for systematically identifying and analyzing the evolution of accident causes. First, accident causal factors are identified and coded based on grounded theory (GT). Then, decision making trial and evaluation laboratory (DEMATEL) is integrated with interpretative structural modeling (ISM) to establish accident evolution hierarchy. Finally, complex networks (CN) are developed to analyze the evolution process of accidents. Compared to reported works, the contribution is threefold: (1) the demand for expert knowledge and personnel subjective influence are reduced through the data induction of accident cases; (2) the method of establishing influence matrix and interaction matrix is improved according to the accident frequency analysis; (3) a hybrid algorithm that can calculate multiple shortest paths of accident evolution under the same node pair is proposed. This method provides a new idea for step-by-step assessment of the accident evolution process, which weakens the subjectivity of traditional methods and achieves quantitative assessment of the importance of accident evolution nodes. The proposed method is demonstrated and validated by a case study of major offshore oil and gas industry accidents caused by storm disasters. Results show that there are five key nodes and five critical paths in the process of accident evolution. Through targeted prevention and control of these nodes and paths, the average shortest path length of the accident evolution network is increased by 35.19%, and the maximum global efficiency decreases by 20.12%. This indicates that the proposed method has broad applicability and can effectively reduce operational risk, so that it can guide actual offshore oil and gas operations during storm disasters.
风暴灾害是海上油气行业事故最常见的原因。为了防止因风暴而导致的事故，分析事故的传播并从以往事故中了解事故机制至关重要。在本文中，提出了一种新颖的风险分析框架，用于系统地识别和分析事故原因的演变。首先，基于扎根理论（GT）识别和编码事故因果因素。然后，将决策试验和评价实验室（DEMATEL）与解释结构模型（ISM）集成，以建立事故演变层次结构。最后，开发复杂网络（CN）来分析事故的演变过程。与已报道的工作相比，本文的贡献有三点：（1）通过事故案例的数据归纳，减少了对专家知识和人员主观影响的需求；（2）根据事故频率分析改进了建立影响矩阵和交互矩阵的方法；（3）提出了一种可以在同一节点对下计算多个事故演变最短路径的混合算法。该方法为逐步评估事故演变过程提供了新的思路，削弱了传统方法的主观性，实现了事故演变节点重要性的定量评估。通过风暴灾害导致的重大海上油气行业事故案例研究对所提出的方法进行了验证。结果表明，在事故演变过程中有五个关键节点和五个关键路径。通过对这些节点和路径进行有针对性的预防和控制，可以将事故演变网络的平均最短路径长度增加 35.19%，最大全局效率降低 20.12%。这表明该方法具有广泛的适用性，可以有效降低运营风险，从而可以在风暴灾害期间指导实际的海上油气作业。
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