A sequence-based method for dynamic reliability assessment of MPD systems
Jingyu Zhu (China University of Petroleum (East China))
Guoming Chen (China University of Petroleum (East China))
Faisal Khan (Memorial University of Newfoundland)
Ming Yang (TU Delft - Safety and Security Science)
Xinhong Li (Xi'an University of Architecture and Technology)
Xiangkun Meng (Dalian Maritime University)
Rui He (China University of Petroleum (East China))
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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.