贝叶斯网络
故障树分析
风险评估
鉴定(生物学)
计算机科学
事件树
风险分析(工程)
钻探
条件概率
可靠性工程
事件(粒子物理)
工程类
统计
数学
人工智能
机械工程
生物
医学
量子力学
植物
物理
计算机安全
作者
Xiangying Meng,Jingyu Zhu,Guoming Chen,Jihao Shi,Tieshan Li,Guozheng Song
标识
DOI:10.1016/j.jclepro.2021.130249
摘要
Risk assessment plays an important role in facilitating the safety and sustainability of deepwater drilling. Managed pressure drilling is increasingly used as an alternative to conventional drilling techniques. This technique increases the complexity and uncertainty of drilling systems with enhancement and advancement of functions. This paper presents a dynamic Bayesian network for risk assessment of managed pressure drilling. The method follows four basic steps including risk identification, topology construction, uncertainty characterization, and consequence evaluation. An event tree-fault tree model was established to develop potential accident scenarios and mapped into a Bayesian network to capture interdependencies and conditional relationships among the contributing factors. Both stochastic uncertainties and fuzzy uncertainties of risk factors are considered in the determination of failure probabilities. Degradation effects of equipment components are included in the forward reasoning of Bayesian network. The case study shows that the initial probability of the blowout is 2.28 × 10−5 and increases to 1.28 × 10−4 in ten time-intervals. Decision makers can take measures to prevent, eliminate, or mitigate accidents of deepwater drilling based on the evaluation results.
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