A sequence-based method for dynamic reliability assessment of MPD systems

Jingyu Zhu, Guoming Chen, Faisal Khan, Ming Yang, Xinhong Li, Xiangkun Meng, Rui He

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Managed Pressure Drilling (MPD) system is widely used in the deepwater drilling operation. Reliability assessment plays a critical role in the MPD system in the management of drilling operation risk and the prevention of blowouts. However, the reliability assessment of the MPD system is challenged due to its sequential operations and multiple processes. Consequently, the present work proposes a sequence-based dynamic reliability assessment method, which focuses on the dynamic modeling of sequential operations for the MPD system by integrating GO-FLOW and dynamic Bayesian Network (DBN). GO-FLOW models are firstly used to define the time interaction between multiple phases for complex systems. A sequence-based mapping method is also proposed for the DBN to construct the reliability model of the MPD system throughout the entire drilling cycle. In the end, the case study analyzed by the proposed framework indicates that the reliability of the MPD system decreases with increasing drilling depth, and the reliability of “tripping in” is highest among four different phases, while the “drilling process” is the lowest. The method provides an important technique that can be implemented with online condition monitoring tools to assess and monitor the reliability of the MPD operation in real-time.

Original languageEnglish
Pages (from-to)927-942
Number of pages16
JournalProcess Safety and Environmental Protection
Volume146
DOIs
Publication statusPublished - 2021

Keywords

  • Deepwater drilling
  • Dynamic bayesian network
  • GO-FLOW method
  • MPD system
  • Reliability assessment

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