Statistical and Probabilistic Analysis-Based RCM-Oriented Cost Optimization Methodology for Oil and Gas Component Assets

Oil and gas facilities operate under demanding conditions where equipment failures can significantly affect production availability, operational safety, and lifecycle costs. Conventional Reliability-Centered Maintenance (RCM) methodologies provide a structured framework for maintenance decision-making; however, their implementation remains largely qualitative and highly dependent on expert judgement. This study proposes an integrated Reliability-Centered Maintenance (RCM) optimization framework that combines Pareto-based criticality assessment, Weibull reliability analysis, and maintenance cost optimization to support quantitative maintenance planning for offshore assets. The framework was applied to centrifugal compressor systems using reliability data obtained from the OREDA database and equipment taxonomy defined by ISO 14224. Critical equipment and maintainable items were identified through Pareto analysis, while Weibull distribution parameters were estimated using maximum likelihood estimation to characterize failure behavior and predict reliability degradation. Age replacement and block replacement models were subsequently employed to determine economically optimal maintenance intervals based on expected cost rate minimization. The results demonstrated optimal preventive age replacement and block replacement intervals of approximately 116,000 and 107,000 operating hours, respectively, for the selected valve failure mode. The proposed methodology enhances traditional RCM by integrating probabilistic reliability modelling and economic optimization within a unified decision-support framework. The findings indicate that the approach can improve maintenance effectiveness, reduce unplanned downtime, and support cost-effective asset management strategies for critical offshore oil and gas equipment.