Nanoparticle-Assisted Polymer Flooding for Enhanced Oil Recovery: Condition-Specific Mechanisms, Modeling Reliability, and Field-Scale Translation

Abstract

Nanoparticle-assisted polymer flooding (NAPF) is increasingly investigated as a hybrid chemical enhanced oil recovery (EOR) strategy because nanoparticles (NPs) can modify polymer rheology, adsorption, interfacial behavior, and flow diversion under selected reservoir conditions. However, reported performance varies substantially with rock mineralogy, permeability, brine salinity, temperature, crude-oil composition, polymer type, nanoparticle chemistry, particle size, concentration, injection sequence, and slug volume. This review critically evaluates NAPF by separating experimentally verified mechanisms from condition-dependent or still-emerging claims. Evidence from rheology, adsorption tests, contact-angle measurements, interfacial-tension analysis, micromodel visualization, and core flooding is synthesized to show when NPs improve polymer flooding and when they may cause aggregation, retention, injectivity loss, or formation damage. The review also distinguishes validated reservoir-simulation approaches from semi-validated pore-scale and molecular models and speculative artificial intelligence (AI)/digital-twin frameworks. Field and pilot-scale reports are compared using standardized descriptors, including reservoir type, permeability, temperature, salinity, nanomaterial concentration, polymer dosage, slug design, injectivity response, water-cut trend, and incremental recovery. Finally, a structured research roadmap is proposed for formulation screening, model validation, field monitoring, techno-economic assessment, and environmental-risk evaluation. The review emphasizes that NAPF should be interpreted as a reservoir-specific technology rather than a universally effective EOR method.