Prediction of Nonlinear Production Performance in Waterflooding Project Using a Multi-Objective Evolutionary Algorithm

Abstract

The paper presents a multi-objective evolutionary algorithm applied to history matching of waterflooding projects, which is to search a feasible set of geological properties showing the reliable future performance. Typical history matching has concentrated on single objective function with linearly weighted terms, even as a realistic field includes many wells and well measurements in time and type. The optimal solution is sensitive to weight factor and competing match criteria of individual term in the objective function often reduce the likelihood of finding an acceptable match. The unacceptable error at a specified well can be observed in a heterogeneous reservoir where shows nonlinear well performances. To overcome the inaccuracy, a new history matching approach is developed that allows the performance characteristics of the whole wells. Individual well performance is optimized separately using genetic algorithm coupled with non-dominated sorting and diversity preservation. The fitness is sorted along to the proximity and then the diversity is added by examining the crowding distance as the approach to arrive at the global optimum. Waterflooding is demonstrated in a heterogeneous oil reservoir with multiple production wells. The predictability of unknown future production performance is compared with that of single objective function, the conventional history matching method. The model represents individualized well-performance more accurately than the conventional history matching. It improves a certainty of the conventional model by showing small error range. The selection of adequate set of reservoir properties is possible among the feasible solutions unlike the conventional model. The developed method can be applied as a useful tool for uncertainty analyses in waterflooding projects.