In this work we consider model-based optimization of polymer flooding. The reservoir performance is optimized by finding for each injection well optimal values for control variables such as injection and production rates, polymer concentrations, and times when to switch from polymer to water injection (i.e. polymer grading). The same technique can also be applied to optimize other EOR processes such as for example designer water flooding, alkali-surfactant polymer (ASP) flooding and foam flooding. The optimization method that has been used relies on the adjoint implementation in our in-house reservoir simulator to efficiently calculate the gradients. The adjoint method enables the computation of gradients with respect to injection and production rates, injection compositions of each well and switching times of each well at the additional cost of approximately the computation time of a single reservoir simulation. The optimization method uses the adjoint-based gradients to estimate the values of all polymer injection control variables that maximize reservoir performance.

The optimization method is demonstrated on a full-field reservoir simulation model. The physics that is modeled includes polymer mixing, hydrodynamic acceleration of the polymer molecules and adsorption of the polymer to the rock. The example shows that the Net Present Value increases significantly as a result of the optimization, mainly due to increased oil production and decreased polymer injection. The obtained optimal control is physically interpreted, so that the learning points from the model-based optimization can be applied to the field and can be used to enhance the polymer flood.

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