Dynamic quantitative risk assessment of accidents induced by leakage on offshore platforms using DEMATEL-BN

• Published in: International journal of naval architecture and ocean engineering Vol. 11; no. 1; pp. 22 - 32
• Main Authors: Meng, Xiangkun, Chen, Guoming, Zhu, Gaogeng, Zhu, Yuan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2019; Elsevier; 대한조선학회
• Subjects: Accident; Bayesian network; DEMATEL; Dynamic; Leakage; Offshore platform; Quantitative risk; 조선공학; Quantitative risk; QRA; Dynamic; DEMATEL; D; CPT; I; BN; M; BP; FT; R; T; X; DRAM; GeNIe; DQRA; f(x); Bayesian network; f(x|data); Offshore platform; Leakage; f; ET; s; Accident; g(data|x)
• ISSN: 2092-6782; 2092-6790
• DOI: 10.1016/j.ijnaoe.2017.12.001

On offshore platforms, oil and gas leaks are apt to be the initial events of major accidents that may result in significant loss of life and property damage. To prevent accidents induced by leakage, it is vital to perform a case-specific and accurate risk assessment. This paper presents an integrated method of Ddynamic Qquantitative Rrisk Aassessment (DQRA)—using the Decision Making Trial and Evaluation Laboratory (DEMATEL)-Bayesian Network (BN)—for evaluation of the system vulnerabilities and prediction of the occurrence probabilities of accidents induced by leakage. In the method, three-level indicators are established to identify factors, events, and subsystems that may lead to leakage, fire, and explosion. The critical indicators that directly influence the evolution of risk are identified using DEMATEL. Then, a sequential model is developed to describe the escalation of initial events using an Event Tree (ET), which is converted into a BN to calculate the posterior probabilities of indicators. Using the newly introduced accident precursor data, the failure probabilities of safety barriers and basic factors, and the occurrence probabilities of different consequences can be updated using the BN. The proposed method overcomes the limitations of traditional methods that cannot effectively utilize the operational data of platforms. This work shows trends of accident risks over time and provides useful information for risk control of floating marine platforms.