A dynamic event tree for a blowout accident in an oil deep-water well equipped with a managed pressure drilling condition monitoring and operation system. (October 2022)

• Record Type: Journal Article
• Authors: Di Maio, Francesco; Baraldi, Piero; Eslamian, Alireza; Zio, Enrico; Jacinto, Carlos Magno Couto
• Is Part Of: Journal of loss prevention in the process industries. Volume 79(2022)
• Publication Date: 2022-10
• Subjects: Risk assessment -- Dynamic event tree -- Blowout probability -- Hybrid Monte Carlo and possibilistic method; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2022.104834
• Languages: English
• ISSNs: 0950-4230

Abstract

Abstract: Blowout is one of the most dreaded accidents for Oil and Gas companies. It is of particular concern during the drilling phase of deep-water oil & gas wells. This is due to the largely uncertain and extremely harsh environmental conditions that affect the design, drilling and operation activities of these wells. Seeking new technological solutions to prevent blowout has led, in the last few decades, to develop Managed Pressure Drilling (MPD) techniques. MPD offers many advantages compared to conventional drilling techniques, such as the capability (1) to detect the gas influx that initiates the kick that might lead to blowout and (2) to optimally control and circulate out this influx, to avoid the blowout. Nevertheless, effects of uncertainties on the MPD functionality are not fully understood and satisfactorily modelled within conventional safety assessment that relies on Event Trees (ETs). In this work, we propose a Dynamic Event Tree (DET) modelling framework of the scenario of kick escalation into blowout to allow accounting for the uncertainties that affect not only the kick variables, but also for the fundamental role played by the time and the delay of the kick detection task. The uncertainties affecting the kick variables are evaluated from kick events records taken from 2000 oil wells drilled in the Niger Delta, whereas the uncertainties affecting the time and delay of kick detection are evaluated by simulating the performance of the tool embedded into the MPD for kick detection (which applies the CUSUM statistical test to differential flow measurements), and assuming a possibilistic distribution for the confirmation time needed by the operator to take counteracting measures with respect to the evolving accidental scenario. A Hybrid Monte Carlo and Possibilistic method is utilized to represent and propagate uncertainties associated to the events occurring throughout the DET. Results are compared with those of a Purely Probabilistic method in support of the blowout probability quantification. Highlights: Blowout accident during drilling phase of deep-water oil&gas wells is considered. A Dynamic Event Tree (DET) modelling framework of the scenario of kick escalation into blowout. The framework accounts for the uncertainties of the kick variables and the delay of the kick detection. A Hybrid Monte Carlo and Possibilistic method is utilized to represent and propagate uncertainties.