Critical Review on the Molecular Behavior of Surfactants at the Oil–Water Interface During Oil Displacement

ABSTRACT While molecular dynamics (MD) simulations have extensively probed surfactant behavior at oil–water interfaces, a dedicated and critical synthesis linking these molecular insights directly to enhanced oil recovery (EOR) efficacy has been lacking. This review fills that gap by systematically integrating existing MD research to elucidate the interfacial behavior of major surfactant classes and their specific roles in reducing interfacial tension and altering interfacial structure for EOR. We uniquely identify key mechanistic knowledge gaps, particularly concerning complex multiphase reservoirs and long‐timescale dynamics. Crucially, this work provides a critical assessment of the strengths and limitations of current MD approaches in addressing EOR‐specific challenges. Looking forward, we propose the integration of emerging computational technologies—specifically multiscale simulations and machine learning—as a novel pathway to bridge molecular‐level understanding with practical surfactant design, ultimately enabling significant improvements in oil recovery. This review thus offers not only a crucial theoretical foundation for optimizing EOR strategies but also new perspectives for future industrial applications by framing a forward‐looking computational roadmap.