Advanced History Matching Techniques Reviewed

Abstract The process of conditioning the geological or the static model to production data is typically known as history matching (HM). The economic viability of a petroleum recovery project is greatly influenced by the reservoir production performance under the current and future operating conditions. Therefore evaluation of the past and present reservoir performance and forecast of its future are essential in reservoir management process. At this point history matching plays a very important role in model updating and hence optimum forecasting, researchers are looking for new techniques, methods and algorithms to improve it. This paper therefore reviews HM and its advancements to date including time-lapse seismic data integration. The paper covers manual and automatic HM, minimization algorithms including gradient and non gradient methods. It reviews the advantages and disadvantages of using one method over the other. Gradient methods covered include conjugate gradient, steepest descent, Gauss-Newton and Quasi-Newton. Non-gradient methods covered includes evolutionary strategies, genetic algorithm and Kalman filter (ensemble Kalman filter).It also addresses re-parameterization techniques including principal component analysis (PCA) and discrete cosine transforms (DCT). The methods are evaluated using a data set based on data from the Norne Field in the Norwegian Sea provided by Statoil and its partners to the Center of Integrated in Petroleum Industry (IO Center) at the Norwegian University of Science and Technology (NTNU).