Nanoparticles assisted polymer flooding: Comprehensive assessment and empirical correlation

Abstract

Conventional Polymer flooding is considered the most promising chemical-enhanced oil recovery by reducing water mobility, and hence increasing the sweep efficiency. However, there are certain drawbacks associated with polymer flooding as the degradation of its molecular structure in severe reservoir environments. Recently, it is experimentally adopted that attachment of nanoparticles (NPs) toward polymer macromolecule may ameliorate the flooding performance and hence increase the oil recovery further than conventional polymer flooding. The potential of NPs assisted polymer flooding has been extensively studied in porous media through core flooding. The target of this work is to statistically scrutinize the previous core flooding experiments and investigate the parameters that affect the incremental oil recovery by NPs assisted polymer flooding. To accomplish this target, a robust investigation of the outcome of over 350 core-flood tests from the literature was gathered to investigate the impacts of rock-oil properties, flooding conditions, polymer, and NPs characteristics on incremental oil recovery. Additionally, Fractional factorial design (FFD) was used to analyze core flood results and screen the effects of reservoir rock, fluid, polymer, and NPs properties on the performance of assisted polymer flooding. Furthermore, an empirical model was built using an Artificial neural network (ANN) to predict the incremental oil recovery for polymeric nanofluid. The results of the investigation indicated that NPs assisted polymer flooding with optimum concentration of both polymer and NPs may result in an incremental recovery of 18% of oil-in-place. Furthermore, the results of FFD revealed that NPs concentration, polymer concentration, and rock permeability are the most significant parameters of oil recovery. The results outcome showed that there is an optimal concentration of both polymer and NPs assisted polymer. In addition, the results revealed that core-flooded viscous oil needs a highly permeable rock to guarantee successful flooding. The coefficient of determination (R2) between the real and calculated incremental oil from the ANN model was established to be 0.953 and 0.952 with an error of 5.6 and 8.7% respectively for the training and testing approaches. Such statistical investigations and ANN approaches afford new insights and guidelines for preliminary assessment, designing, and execution of Nanohybrid polymer upcoming projects on a field scale.