Application of dynamic risk analysis in offshore drilling processes. (November 2020)

• Record Type: Journal Article
• Authors: B, Bijay; George, Priscilla; Renjith, V.R.; Kurian, Anish Job
• Is Part Of: Journal of loss prevention in the process industries. Volume 68(2020)
• Publication Date: 2020-11
• Subjects: Bow-tie analysis -- Dynamic risk assessment -- Bayesian network -- Kick -- Drilling; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2020.104326
• Languages: English
• ISSNs: 0950-4230

Abstract

Abstract: Process safety is the common global language used to communicate the strategies of hazard identification, risk assessment and safety management. Process safety is identified as an integral part of process development and focuses on preventing and mitigating major process accidents such as fires, explosions, and toxic releases in process industries. Accident probability estimation is the most vital step to all quantitative risk assessment methods. Drilling process for oil is a hazardous operation and hence safety is one of the major concerns and is often measured in terms of risk. Dynamic risk assessment method is meant to reassess risk in terms of updating initial failure probabilities of events and safety barriers, as new information are made available during a specific operation. In this study, a Bayesian network model is developed to represent a well kick scenario. The concept of dynamic environment is incorporated by feeding the real-time failure probability values (observed at different time intervals) of safety barriers to the Bayesian network in order to obtain the corresponding time-dependent variations in kick consequences. This study reveals the importance of real-time monitoring of safety barrier performances and quantitatively shows the effect of deterioration of barrier performance on kick consequence probabilities. The Macondo blowout incident is used to demonstrate how early warnings in barrier probability variations could have been observed and adequately managed to prevent escalation to severe consequences. Highlights: Development of a Bayesian network model representing an offshore well kick scenario. The time-dependent variations of failure probability values of identified safety barriers are fed to the Bayesian network to obtain corresponding time-dependent variations in kick consequences. The effect of deterioration of barrier performance on kick consequence probabilities are studied. Study reveals the importance of real-time monitoring of safety barrier performances.