A novel methodology to analyze accident path in deepwater drilling operation considering uncertain information

Xiangkun Meng, Xinhong Li, Weigang Wang, Guozheng Song, Guoming Chen and Jingyu Zhu

Reliability Engineering and System Safety, 2021, vol. 205, issue C

Abstract: An initial failure in a vulnerable part of deepwater drilling system may escalate into major accidents such as blowout, fire, or explosion. Such accidents have characteristics of complexity, dynamics, and uncertainty, which traditional risk assessment methods fail to capture. This paper presents an integrated methodology for evaluating deepwater drilling risk by combining directed acyclic graph (DAG) and risk entropy. The methodology follows four basic steps: identifying risk factors, defining failure scenarios, determining failure probabilities and entropy values, and evaluating the most probable path of failure events. A network topology is established to develop the possible accident scenarios and paths. Risk entropy is then applied to handle both technical failures and human errors. Bayesian theory is used to describe the dynamics of random factors. The shortest path that represents the most probable failure path from an initial event to a blowout accident is further calculated using Dijkstra algorithm. The proposed approach is then applied in a case study about a managed pressure drilling (MPD) system. The result shows that changes of uncertainties of risk factors result in the variation of the shortest path both in probability values and event sequences. Hence the targeted measures can be implemented according to the assessment result.

Keywords: System risk; Deepwater drilling; Network; Risk entropy; Bayesian theory; Uncertainty modeling (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:205:y:2021:i:c:s0951832020307559

DOI: 10.1016/j.ress.2020.107255

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