The oil and gas industry faces mounting pressure to enhance recovery rates while reducing operational costs and environmental impact. Waterflooding, a dominant secondary recovery method, accounts for nearly 50% of global improved oil recovery (IOR) projects (IEA, 2023). However, conventional waterflooding suffers from inefficiencies such as poor sweep efficiency, premature water breakthrough, and suboptimal injection strategies. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a game-changer in optimizing waterflooding operations. AI enables: Real-time reservoir surveillance through IoT sensors and automated data analytics.Predictive modeling for water breakthrough and injection optimization.Enhanced reservoir characterization using seismic and production data.Economic optimization by reducing downtime and improving recovery factors. This paper provides a detailed review of AI applications in waterflooding, supported by field case studies and economic analyses.
Chen et al. (2025) studied this question.