A Comparative Study of Proxy Modeling Techniques in Assisted History Matching

Abstract

Abstract Assisted history matching approach has been arisen in the last few decades in an attempt to make the process of history matching faster and easier. Assisted history matching simply involves converting the history matching problem to an optimization problem. One main aspect of the assisted history matching is building proxy model that interpolates the relationship between the objective function and the history matching parameters. Several proxy modeling techniques are introduced in the literature, some are useful and some are not. This paper provides a comparative study between four powerful proxy modeling techniques in assisted history matching; Thin Plate Spline, Radial Basis Function, Kriging, and Artificial Neural Network. Two test problems of different reservoir engineering approaches (material balance and reservoir simulation) are used to test and compare the performance of the studied proxy methods on solving assisted history matching problems. To make the comparison reliable, a performance indicator is developed to compare between the four studied techniques. The performance indicator represents the relative error between the estimated values of history matching parameters calculated using the studied proxy modeling methods and their exact solutions. The results of this work indicate that the Kriging and artificial neural network proxy techniques are more efficient and superior to thin plate spline and radial basis function.