Numerical simulation of polymer flooding in a heterogeneous reservoir - Constrained vs unconstrained optimization

This study investigates how a polymer flood design can be optimized while considering geological uncertainty in the reservoir models. Polymer flooding can increase oil recovery, reduce water cut, and improve sweep efficiency by diverting flow to low permeable zones. We applied two different history matching approaches (manual and gradient-based) to match data from a prolonged waterflood in the Watt field, a synthetic but realistic clastic reservoir that is based on real data and captures a wide range of geological heterogeneities and uncertainties through a range of different model scenarios and model realizations. The subsequent polymer flood is then optimized using history-matched models (constrained optimization) and the original, non-history-matched models (unconstrained optimizations). The aim is to study how geological uncertainties innate in clastic reservoir affect polymer flooding, and how different history matching approaches impact the predicted reservoir performance and optimal polymer flood design. A key observation is that shale cut-offs were a major uncertainty when optimizing the polymer flood for this field. In addition, the constrained optimization gave a much narrower forecast for incremental oil recovery during polymer flooding, possibly underestimating both risk and economic opportunities.