Qualitative Fault Tree Analysis of Blowout Preventer Control System for Real Time Availability Monitoring

Abstract

This paper deals with the dynamic availability of a subsea blowout preventer (BOP) with respect to regional and industrial requirements. The study aims to reduce the non-productive time on drilling rigs due to complex propagation of failures within the BOP and BOP control system. In this development, fault tree analysis updated with near real time failure database is implemented into operational decision-making process. First, the system is defined with specifications, boundaries and assumptions. Failure modes of each component are stated. The requirements on the BOP control system are determined with respect to API Standard 53. Fault trees are constructed to address each requirement, and cut sets are produced. For dynamic analysis, a database on BOP state and failure component is created using reports from drilling contractor and operator. Upon detection of a failure, the cut sets are updated. With this application, impact of newly discovered failure in combination with the existing set of failures over BOP availability and compliance is determined in a standardized manner allowing consistent and efficient judgment.

Subsea blowout preventer (BOP): Design, reliability, testing, deployment, and operation and maintenance challenges

“…Holand and Rausand [33] used it in conjunction with failure reports as a means of assessing the reliability of subsea BOPs. Mutlu et al [84] performed a qualitative FTA to assess the reliability of a BOP control system. In another study, Zhang et al [77] discussed its application in combination with fuzzy analysis theory to determine the reasons for failure of an annular BOP.…”

Providing a comprehensive approach to oil well blowout risk assessment

“…Several studies have been done using different tools to assess the risk of process industries and different tools have been used to calculate blowout probabilities. The studies of Cai et al [ 26 , 27 ], Mutlu et al [ 28 ], Meng et al [ 29 , 30 ], Chang et al [ 31 ], and Liu et al [ 5 ] are examples of cases where BN and dynamic Bayesian network (DBN) were used for blowout risk assessment. Di Maio and colleagues also used the Dynamic Event Tree (DET) combined approach with BN to analyze the blowout accident in an oil deep-water well [ 32 ].…”