Quantitative risk analysis of offshore drilling operations: A Bayesian approach
Quantitative risk analysis of offshore drilling operations: A Bayesian approach
复制标题
DOI：
10.1016/j.ssci.2013.01.022
复制
发表时间：
2013-08-01
期刊：
影响因子：
6.1
通讯作者：
Amyotte, Paul
中科院分区：
文献类型：
--
作者：
Khakzad, Nima;Khan, Faisal;Amyotte, Paul
井喷是钻井作业中最不希望发生和最令人担心的事故之一。井喷事故的动态性质，由于快速变化的物理参数和随时间变化的故障的障碍，需要技术能够考虑时间的依赖性和变化，在一个井的寿命。目前的工作是旨在展示应用蝴蝶结和贝叶斯网络方法进行定量风险分析的钻井作业。考虑到前一种方法，故障树和事件树的潜在事故场景的开发，然后结合建立一个蝴蝶结模型。在后一种方法中，首先，个别贝叶斯网络的事故场景开发，最后，一个面向对象的贝叶斯网络是通过连接这些个别网络。贝叶斯网络方法提供了比蝴蝶结模型更大的价值，因为它可以考虑共因故障和条件依赖性沿着执行概率更新和顺序学习使用事故前兆。(C)2013爱思唯尔有限公司保留所有权利。
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. (C) 2013 Elsevier Ltd. All rights reserved.