Numerical Simulation of Polymer Flooding in a Heterogeneous Reservoir: Constarained versus Unconstrained Optimization

The average global recovery factor of a typical oil and gas field is approximately 40% at after secondary recovery processes such as gas lifting and water flooding. The low recovery factor is often a result of by passing a considerable amount of oil in the reservoir due to unknown reservoir heterogeneity and incomplete understanding of the geology. Enhanced oil recovery (EOR) methods will play a key role in increasing recovery factors from existing reservoirs. Heterogeneous reservoir sands often show permeability contrast between layers and therefore lead to early water breakthrough when water flooding. In such situations, polymer flooding can potentially be a suitable EOR technique that helps to lower water cut and increase recovery. The addition of polymers to the injected water lowers the mobility ratio, thereby reducing viscous fingering and delaying water breakthrough. This study investigates how a polymer flood design can be optimized while considering geological uncertainty in the reservoir models as well as modelling decisions. We applied an adjoint based technique 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. We apply Latin hypercube experimental designs with the Particle Swarm Optimization algorithm in CMOST. This was used to build a proxy model employing polynomial regression for the optimization of the engineering parameters to maximize NPV. The optimization were performed for both history-matched models (constrained optimization) and the original, non-history-matched models (unconstrained optimization). The aim of this work is to analyse how geological uncertainties inherent to a heterogeneous clastic reservoir as well as modelling decisions impact the design and performance a polymer flood. We further investigate how the different optimization methods impact the predicted reservoir performance and optimal design of the polymer flood. Our findings show that both, geological and engineering uncertainties, impact polymer flooding and that designing the right well controls is essential for successful polymer flooding. Shale cut-offs are identified as a key petrophysical uncertainty when optimizing a polymer flood in a heterogeneous clastic reservoir. Furthermore, forecasts using constrained optimization yielded a much narrower range of incremental oil recovery and NPV during polymer flooding and may underestimate both, risk and opportunities for polymer flooding because the history matching of the water flood emphasizes different geological features compared to the way geology interacts with a more viscous polymer solution.