An optimization method for the assisted history matching (AHM) process using the gradient boosting approach

In this article, we propose a machine-learning approach, which is aimed to filter out the redundant reservoir models prior to simulations during the assisted history matching (AHM). The redundant models may be generated during AHM due to arbitrary switch of the flow simulation well control for a particular combination of reservoir model parameters. This aproach allows to save CPU time and increase efficiency of AHM process. Optimization algorithms used in AHM to iterate through possible combinations of model parameters trying to minimize an objective function may lead to unrealistic parameters combinations [2]. Specifically, some parameters combinations may results in reservoir models automatically switch from under bottom hole pressure control due to insufficient productivity properties of the model. As a result, AHM will generate many simulations, which are initially wrong and have no chance to match the history. We created an approach which is able to classify if the model parameters will lead to switching under bottom hole pressure control or not before running a flow simulations.