A hybrid differential evolution algorithm approach towards assisted history matching and uncertainty quantification for reservoir models

History matching is an important process in the reservoir model development. In the process of history matching, the most significant uncertain model parameters are identified and adjusted to get an acceptable match between the simulated production with the historical field production data. In the past decade, many population based algorithms have been applied for history matching. In this paper, a novel population based stochastic algorithm called hybrid differential evolution (HDE) is applied for the assisted history matching process. An adaptive mechanism for the control parameters is incorporated in the algorithm which automatically adjusts the control parameters according to the problem. The performance of the algorithm is tested on a 3-D reservoir model called PUNQ-S3 which is a benchmark model for the comparison of different history matching and uncertainty quantification techniques. Since history matching is an inverse problem, multiple models can give good match. So, prediction using a single history matched model involves more risk because of the parameter uncertainty. One of the methods to solve this problem is to quantify the uncertainty in the predictions. In this paper, the neighbourhood approximation Bayes (NAB) algorithm is applied to quantify the uncertainty in reservoir forecast which is a Bayesian extension of neighbourhood algorithm. The NAB algorithm quantifies the uncertainty in the predictions using multiple models generated during history matching phase and this does not require additional simulations. The main focus of this paper is to study about how HDE algorithm can be used when coupling with the NAB algorithm in predicting the true forecast with minimum uncertainty range under limited number of simulations. The influence of population size on the performance of the algorithm in history matching and forecast is analyzed. The HDE provides wide sampling of the search space and the truth case was comfortably included within the predicted confidence bounds. The results show that HDE can be used as a promising tool for assisted history matching of the reservoir models. (C) 2016 Elsevier B.V. All rights reserved.