Blowouts are among the most undesired and feared accidents during drilling operations. The dynamic nature of blowout accidents, resulting from both rapidly changing physical parameters and time-dependent failure of barriers, necessitates techniques capable of considering time dependencies and changes during the lifetime of a well. The present work is aimed at demonstrating the application of bow-tie and Bayesian network methods in conducting quantitative risk analysis of drilling operations. Considering the former method, fault trees and an event tree are developed for potential accident scenarios, and then combined to build a bow-tie model. In the latter method, first, individual Bayesian networks are developed for the accident scenarios and finally, an object-oriented Bayesian network is constructed by connecting these individual networks. The Bayesian network method provides greater value than the bow-tie model since it can consider common cause failures and conditional dependencies along with performing probability updating and sequential learning using accident precursors. © 2013 Elsevier Ltd.

Item Type:	Refereed Article
Keywords:	Blowout; Bow-tie approach; Drilling; Kick; Object-oriented Bayesian network; Risk analysis; Accident precursors; Accident scenarios; Bayesian approaches; Bayesian network methods; Bow tie; Common cause failure; Drilling operation; Dynamic nature
Research Division:	Engineering
Research Group:	Maritime engineering
Research Field:	Ocean engineering
Objective Division:	Mineral Resources (Excl. Energy Resources)
Objective Group:	Environmentally sustainable mineral resource activities
Objective Field:	Environmentally sustainable mineral resource activities not elsewhere classified
UTAS Author:	Khan, F (Professor Faisal Khan)
ID Code:	94465
Year Published:	2013
Web of Science® Times Cited:	248
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2014-09-09
Last Modified:	2017-11-03
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