A challenging problem of automated history-matching work flows is ensuring that, after applying updates to previous models, the resulting history-matched models remain consistent geologically. This is particularly challenging in formations with complex connectivity patterns. In this work, the authors introduce a novel machine-learning approach with the aim of preserving the main connectivity patterns of previous reservoir models during history matching of complex geologic formations.
Introduction
The authors introduce a machine-learning algorithm to incorporate discrete connectivity patterns in history matching of complex-geologic-facies models. This is achieved by splitting the introduced history-matching optimization problem into two iterative subproblems: a continuous approximation of the solution that is obtained by solving a regularized least-squares inversion (while maintaining the expected connectivity of the patterns) followed by a machine-learning-based mapping of the continuous solution to the discrete feasible set defined by previous models.
