Influence of the Kalman gain localization in adaptive ensemble smoother history matching

Abstract

In reservoir engineering, history matching is the technique of conditioning a reservoir simulation model to the available production data. The reservoir properties have uncertainties which lead to discrepancies between the observed data and the reservoir simulator response, making history matching an indispensable tool in the petroleum industry. The standard Ensemble Smoother with Multiple Data Assimilation (ES-MDA) application has become popular in history matching problems. However, in the standard methodology, the number of iterations must be previously defined by the user, what makes it a determinant parameter in the ES-MDA results. One way to solve this problem is to perform adaptive algorithms: these algorithms keep iterating until they reach desirable matchings with the real data. Furthermore, to apply this method in large-scale reservoirs, it is necessary to use some localization technique to prevent spurious updates and high uncertainty reduction after the ES-MDA is applied. This work evaluates the influence of the distance-based Kalman Gain localization in an adaptive Ensemble Smoother, by applying the mentioned methodology in a large-scale synthetic reservoir model. The experiments showed a connection between the localization parameters and the number of iterations required by the adaptive algorithm. Moreover, the results presented significant reduction in the production data mismatch, regardless the localization, being the mismatch of the prior and posterior models an important parameter to determine the history matching quality.