Abstract
Offshore drilling operations may not be safe nor available if they are not well maintained. Dynamic risk-based maintenance (RBM) methodology is a tool for scheduling maintenance plans based on an acceptable level of risk. It is applied to improve the safety and reliability of systems assisting in identifying and prioritizing the maintenance of critical components. This paper proposes an advanced RBM methodology for the design of optimum maintenance programs. Bayesian Networks (BNs) are employed to develop the dynamic RBM models that are capable of using accident precursor information in order to update the risk profile. The developed methodology is applied to a case study of an offshore MPD operation considering two critical systems: rotating control device (RCD) and blowout preventer (BOP). The obtained results show that the minimal length of maintenance intervals for a RCD valve was 18 days while being 23 days for the BOP valve. Sensitivity analysis is also conducted to evaluate the contribution percentage of each component to the system failure. The sealing elements for both RCD and BOP systems require additional attentions since they were determined as the most critical components in the BN model.
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