Polymer flooding dates from the 1960s. Early applications targeted onshore medium-to-heavy oils up to 100 cP, with limited reservoir temperature and water formation salinity. The number of implemented polymer flooding projects followed oil prices. Since its early days, polymer flooding had overcome many technical obstacles. Advances in polymer manufacturing technology, cost reduction and the use of horizontal wells have pushed polymer flooding as a feasible EOR method. A better understanding of the physical phenomena associated with polymer flow through porous media and technology advancement have extended polymer flooding applications to more viscous oil, higher salinity, and temperature level, as well as to offshore prospects. Meaningful advantages of polymer flooding over conventional methods are consolidated in the literature, such as oil recovery anticipation, incremental oil recovery and reduced volumes of injected and produced water to reach a target recovery factor. Despite all technological advances, polymer flooding needs to be tailored for the specific conditions of the target reservoir. Collect and integrate laboratory, simulation, and field information are essential for a successful polymer flooding application. This paper aims to correlate critical information to the various stages necessary for polymer flooding evaluation and production forecast. First, successfully implemented field cases allow the establishment of ranges for the method application. Once the applicability of polymer flooding is certified, the polymer solution to be injected is designed according to the reservoir characteristics and target conditions. Laboratory tests are performed to determine phase mobilities, polymer retention, and polymer degradation. These parameters are assessed through different experiments, and normalized variables provide data integration. Once the required parameters are determined, it is possible to build a base simulation model. History matching this base model to the laboratory data certifies its validity. An upsized analysis of this model is required to include some degradation phenomena. The 1D laboratory model is extended to a 3D model that incorporates permo-porosity distributions to analyze well characteristics in their radius of influence. The final step is large scale simulation and production forecast. Data integration along each stage and among then all allow the tailoring of the polymer flooding to EOR. The use of normalized parameters to evaluate the results is useful for analysis at different scales, from the laboratory to the reservoir. The proposed workflow can contribute to the design, planning, evaluation, and implementation of polymer flooding in a target field.

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