Experimental and Computational Fluid Dynamics Investigation of Mechanisms of Enhanced Oil Recovery via Nanoparticle-Surfactant Solutions

The enhancement in surfactant performance at downhole conditions in the presence of nanomaterials has fascinated researchers' interest regarding the applications of nanoparticle-surfactant (NPS) fluids as novel enhanced oil recovery (EOR) techniques. However, the governing EOR mechanisms of hydrocarbon recovery using NPS solutions are not yet explicit. Pore-scale visualization experiments clarify the dominant EOR mechanisms of fluid displacement and trapped/residual oil mobilization using NPS solutions. In this study, the influence of multiwalled carbon nanotubes (MWCNTs), silicon dioxide (SiO2), and aluminum oxide (Al2O3) nanoparticles on the EOR properties of a conventional surfactant (sodium dodecyl benzene sulfonate, SDBS) was investigated via experimental and computational fluid dynamics (CFD) simulation approaches. Oil recovery was reduced with increased temperatures and micromodel heterogeneity. Adding nanoparticles to SDBS solutions decreases the fingering and channeling effect and increases the recovery factor. The simulation prediction results agreed with the experimental results, which demonstrated that the lowest amount of oil (37.84%) was retained with the micromodel after MWCNT-SDBS flooding. The oil within the micromodel after Al2O3-SDBS and SiO2-SDBS flooding was 58.48 and 43.42%, respectively. At 80 degrees C, the breakthrough times for MWCNT-SDBS, Al2O3-SDBS, and SiO2-SDBS displacing fluids were predicted as 32.4, 29.3, and 21 h, respectively, whereas the SDBS flooding and water injections at similar situations were at 12.2 and 6.9 h, respectively. The higher oil recovery and breakthrough time with MWCNTs could be attributed to their cylindrical shape, promoting the MWCNT-SDBS orientation at the liquid-liquid and solid-liquid interfaces to reduce the oil- water interfacial tension and contact angles significantly. The study highlights the prevailing EOR mechanisms of NPS.