A Bayesian network based method for reliability analysis of subsea blowout preventer control system. (May 2019)

• Record Type: Journal Article
• Authors: Liu, Zengkai; Liu, Yonghong
• Is Part Of: Journal of loss prevention in the process industries. Volume 59(2019)
• Page Start: 44
• Page End: 53
• Publication Date: 2019-05
• Subjects: Reliability analysis -- Bayesian network -- GO model -- Fault tree -- Subsea blowout preventer; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2019.03.004
• Languages: English
• ISSNs: 0950-4230

Abstract

Abstract: Subsea drilling needs extremely complex equipment, but engineering challenges are posed by harsh offshore environments. A subsea blowout preventer (BOP) is a critical device used to seal, control and monitor oil wells to ensure safety during the drilling process. In this paper, a novel approach to construct an equivalent Bayesian network (BN) from a GO model is proposed and it is used for assessing reliability of the subsea BOP control system. The proposed method relaxes some limitations of GO models and enriches the ways of developing BNs. Probability updating and adaption of the presented BN is demonstrated by case studies. A three-axiom-based validation method is used for partial validation of the BN. The proposed method can be applied to other existing GO models. Highlights: A novel approach for constructing BNs from GO methodology is proposed. Reliability analysis of the electrical control system of subsea BOP is performed. The developed BN is capable of probability updating and adapting.