Polymer flooding has been widely studied and applied for enhancing reservoir oil recovery. The key to evaluating the potential application of polymer injection is based on a clear understanding of the mechanisms involved in the recovery process. This work focuses on history matching of laboratory core flooding of heavy oil recovery by polymer flooding. We match the experiments through a small-scale simulation model and evaluate the challenges of representing the polymer behavior in porous media. The methodology we propose in this paper aims to model core-flooding experiments with laboratory-measured data. These data come from different experiments, including rheology, single-and two-phase core flooding and are separated into consolidated and uncertain data. Consolidated data include: core volume, permeability, porosity, oil viscosity and density, initial saturation conditions, test temperature and injection rate. The uncertain input parameters involve more complex variables, such as viscosity behavior against shear rate and concentration, residual resistance factor, adsorption, inaccessible pore volume and water-oil relative permeabilities. We compare the simulation output parameters with laboratory results and evaluate the quality of the match for four different two-phase core-flooding experiments. The output parameters include histories for differential pressure, recovery factor, water cut and cumulative produced water, oil and liquid. Our results show that the simulation models successfully matched the experimental data, for all the cases, using appropriate representation of the laboratory parameters. The contribution of this work relies on the proposed methodology, which allows integrating the laboratory data in the construction and validation of core scale simulation models appropriately.

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Polymer flooding has been widely used for enhancing oil recovery, due to the growing number of successful applications around the world. The process aims to increase water viscosity and, thus, decrease the water/oil mobility ratio, thereby improving sweep efficiency. The understanding of the physical mechanisms involved in this enhanced oil recovery process allows us to forecast the application potential of polymer flooding. This work aims to assess physical phenomena associated with heavy oil recovery through polymer flooding using 1D small-scale simulation models. We evaluate the influence of different levels of adsorption, accessible pore volume, residual resistance factor, and polymer concentration on the results and compare their magnitude effect on the results. The models used in this study were built using data from previous lab work and literature. For each one of the mentioned parameters, this work compares the histories of water cut, cumulative water-oil ratio, average pressure, and oil recovery factor. Additionally, water saturation, water viscosity, and water mobility profile were determined for specific periods of the flooding process. The sensitivity analyses showed that high levels of adsorption influence the polymer loss of the advance front, delaying oil recovery. Low values of accessible pore volume lead to a slightly faster polymer breakthrough and oil recovery anticipation. A high residual resistance factor increases the average pressure and improves oil recovery. Higher polymer concentration enhances the displacement efficiency and enhances the recovery factor.