Dynamic and quantitative risk assessment under uncertainty during deepwater managed pressure drilling. (1st February 2022)

• Record Type: Journal Article
• Authors: Meng, Xiangkun; Zhu, Jingyu; Chen, Guoming; Shi, Jihao; Li, Tieshan; Song, Guozheng
• Is Part Of: Journal of cleaner production. Volume 334(2022)
• Publication Date: 2022-02-01
• Subjects: Managed pressure drilling -- Quantitative risk analysis -- Dynamic bayesian network -- Stochastic uncertainty -- Fuzzy uncertainty; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2021.130249
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: 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. Highlights: A dynamic and quantitative model is proposed to assess deepwater drilling risks. Expert opinions are translated into probabilities to deal with fuzzy uncertainty. Probability distribution function is used to calculate stochastic uncertainty. Equipment degradation is considered in forward reasoning of Bayesian network.