Optimization of Polymeric Nanofluid Oil Recovery Mechanisms at Reservoir Condition

Abstract Nanoparticles have been used to improve the properties of oilfield polymers however, at higher and prolonged temperature, dihydroxylation of the bonds occurs decreasing the affinity of the oilfield polymers towards the surface of the nanoparticles. Hence, polymeric nanoparticles (LPNP) with rigid structures have been sought after to circumvent this problem. Therefore, in this study, LPNP was synthesized from sago palm (Metroxylon sagu) bark and used as a rheological agent to improve the viscosity of displacement fluids. Thereafter, the synthesized LPNP was characterized via transmission electron microscopy, particle size analysis, zeta potential, Fourier transform infrared spectroscopy and thermogravimetric analysis. Then, the rheological flow behaviour of lignin polymeric nanofluid (LPNF) was investigated at low and high shear rates utilizing a 350 RST Brookfield rheometer. Box-Behnken design was used to simulate the effect of salinity, shear rate, concentration, and temperature on the viscosity of LPNF. Statistical analysis of variance was used to analyse various parameters of the model. Finally, any parameter combination that resulted in the maximum viscosity was recorded and optimized using a multi-response surface model. The synthesis method was efficacious in producing LPNP with a size range of 10–23 nm. Besides, LPNF exhibited shear thinning and pseudoplastic behaviours even at high salinity and showed good stability up to a temperature of 170°C. The predicted viscosity with a regression coefficient (R2) of 0.8 indicates that the experimental data were accounted for by the model. The desirability of 0.95, which is close to unity, indicates that statistical analysis and experimental evidence have demonstrated that LPNF has acceptable flow behaviour under reservoir conditions.