Abstract This study presents a novel hybrid methodology combining machine learning (ML) with conventional reservoir simulation to optimize waterflooding in the geologically complex Bahariya Formation, Western Desert, Egypt. The research addresses the critical need for accurate oil recovery efficiency (RF%) prediction by developing and deploying robust data-driven models. Linear regression quantified the impact of nine key parameters across three injection patterns.
