Electrical Submersible Pumps (ESPs) are widely used to sustain oil production in mature and challenging reservoirs; however, their frequent failures often lead to production deferment, costly workovers, and reduced operational efficiency. This study presents a comparative evaluation of predictive maintenance (PM) and reactive maintenance for ESP systems using 50 case studies from diverse reservoirs and operating conditions. Reliability performance was assessed using mean time between failures (MTBF), mean time to failure (MTTF), failure rate, Kaplan–Meier survival analysis, and Weibull probability modeling. The economic impact of maintenance strategy was also evaluated through failure-related costs and deferred production losses.

The results indicate that PM consistently enhances ESP reliability by extending operational run life and reducing failure occurrence. Median survival increased from approximately 400 days before PM implementation to about 750 days after PM adoption, while Weibull analysis showed a shift from early random failures toward more predictable wear-out behavior. Economic evaluation further demonstrated that PM reduces workover frequency and production-loss exposure, yielding estimated cost savings of approximately 30–40% across the analyzed cases, even after accounting for monitoring and diagnostic investments.

This study contributes to the ESP reliability literature by providing an integrated comparison of predictive and reactive maintenance using survival analysis, Weibull modeling, and economic assessment within a unified framework across multiple field cases. The findings confirm that predictive maintenance is both technically effective and economically justified, and they highlight the value of combining advanced monitoring, reliability analytics, and data-driven maintenance planning to improve ESP performance and reduce life-cycle costs in oilfield operations.