Development of a Data-Partition Technique for Gradient-Based Optimization Methods in History Matching

Abstract

Summary Assisted history matching is now widely used to constrain reservoir models. However, history matching is a complex inverse problem, and it is always a big challenge to history match large fields with a large number of parameters. In this paper, we present a new technique for the gradient-based optimization methods to improve history matching for large fields. This new technique is based on data partition for the gradient calculations. In history matching, the objective function can be split into local components, and a local component generally depends on fewer influential parameters. On the basis of this decomposition, we can propose a perturbation design, which allows us to calculate all derivatives of the objective function with only a few perturbations. This method is particularly interesting for regional and well-level history matching, and it is also suitable to match geostatistical models by introducing numerous local parameters. This new technique makes history matching with a large number of parameters (large field) tractable.