Global/local optimization strategies combined for waterflooding problems

Abstract

In oil Reservoir Engineering application, a problem of great interest is the dynamic optimization of waterflooding management. In this work, the net present value (NPV) is taken as the function to be maximized, in which the allocated rates of wells are considered as design variables. Alternatively, the switching times of the control cycles can also be considered as design variables. This assumption increases flexibility to the management. Despite this, the formulation of this problem leads to a highly nonlinear, multimodal objective function. Therefore, to conduct the management, a hybrid optimization strategy is proposed here considering surrogate models. The hybrid strategy combines different methods at two different stages, a global and local. In this sense, the global search is driven by the genetic algorithm (GA) and the local search is driven by the sequential approximation optimization (SAO) method. The proposed methodology was successful in identifying wells that should be late started or shut-in before the end of the concession period and in handling different kinds of production strategies. It was also verified that an increasing on operation flexibility results in NPV improvement. Cycle duration variables proved to be useful in decreasing the number of design variables while maintaining recovery efficiency.