Scatter Search Metaheuristic Applied to the History-Matching Problem

Abstract

Abstract Reservoir simulation plays an important role in managerial decisions during the life of a reservoir, so it is vital that they present an accurate prospect of real reservoir performance. The main goal of the History Matching (HM) process is to improve the quality of numerical models by constraining simulated to observed data. A typical HM process evaluates a chosen objective function (OF) comprised by linear functions on the uncertain reservoir properties. The OF can be modeled in such a way that the HM process can be solved as an optimization problem. This paper discusses how the Scatter Search (SS) technique can solve the HM problem. The main feature of SS is that it works on a set of solutions called the reference set (RefSet). The idea is to improve the overall quality of the RefSet. New solutions are generated by a non-convex combination of explored solutions. The goal of this paper is solve the HM with SS and to evaluate its performance. The proposed methodology was tested with two base synthetic reservoir models. The first is a homogenous reservoir with 8 different horizontal permeability regions, while the second is a highly heterogeneous reservoir model where low quality background sand is crossed by high permeability canals. The results show that SS was quite efficient, considering the quality of the generated solutions and the number of required numerical simulations. Most of the current HM methodologies do not perform well when the solution space is large and complex. The application of the SS methodology to the HM problem is a novel approach. Unlike most metaheuristics, SS can be effective even when the simulation time of each tentative solution to the problem is long.