Advanced History Matching Techniques Reviewed
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The process of conditioning the geological or the static model to production data is typically known as history matching. 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 history matching 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 includes 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 address re-parameterization techniques including principal component analysis (PCA) and discrete cosine transforms (DCT). Different case studies are referred as performed using the Norne field data in the North Sea provided by the Center of Integrated in Petroleum Industry (IO Center) at the Norwegian University of Science and Technology (NTNU).
The results from any simulation model should be used with caution and the degree of confidence depends on the initial objective of the user, however how fast the simulation is and the overall quality of the model remain important.http://www.iocenter.no/node/add/publication
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