Workflow of molecular dynamics simulations in polymer design and optimization.

Relative permeability of polymer flooding plays a very important role in oil field development. This paper aimed to measure and calculate the relative permeability curves of polymer flooding more accurately. First, viscosity variation law of polymer in porous media was studied. Rock particles of different diameters and cementing agent were used to make artificial cores and hydrophobically associating polymer solutions were prepared for experiments. Polymer solutions were injected into the cores filled with crude oil and irreducible water. In the process of polymer flooding, produced fluid was collected at different water saturations and locations of the core. Polymer solutions were separated and their viscosities were measured. With the experimental data, the viscosity variation rule of polymer transporting in porous media was explored. The result indicates that the viscosity retention rate of polymer solutions transporting in porous media has power function relationship with the water saturation and the dimensionless distance from the core inlet. Finally, the relative permeability curves of polymer flooding were measured by unsteady state method and the viscosity variation rule was applied to the calculation of the relative permeability curves.

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.