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• W2051257269 abstract "Abstract History matching is an inseparable part of reservoir modelling in which flow performance data are incorporated into geological model to reduce uncertainty in decision making. Although, thus far numerous studies have been published in order to augment the quality of history matching, a comprehensive approach is not being taken toward it, and development in different steps of history matching, including reparameterization and optimization, is looked for. Reparameterization is used to reduce the number of decision variables and subsequently the degree of calibration freedom in order to expedite history matching procedure. But, it always brings reparameterization error into the problem. In this study, an inventive algorithm is introduced in which reparameterization is unnecessary and accordingly the corresponding error is eliminated. In this algorithm, entire uncertain parameters including porosity and permeability distributions can directly be adjusted. Image fusion technique is widely used in image processing problems. Image fusion is a technique for combining a number of images into a single image to afford a more informative image. In this study, image fusion technique is applied in history matching problems. In the designed algorithm, different models intelligently and stochastically are merged based on their corresponding fitness values to produce a fitting model. It is a repetitive approach until stopping criteria are met. Hence, it performs similar to evolutionary algorithms while image fusion works as a mating operator. In order to assess the proposed algorithm, the outcomes of history matching using this algorithm on a synthetic model and also PUNQ-S3 reservoir model are compared with a number of history matching approaches. The outcomes demonstrate an improvement over the applied approaches in terms of the quality of the history matched models. The proposed approach is coped with a significantly larger number of variables, and accordingly high resolution history matched models are achieved. Also, the algorithm has an acceptable speed of convergence even in comparison with variable metric algorithms." @default.
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• W2051257269 date "2013-06-10" @default.
• W2051257269 modified "2023-10-17" @default.
• W2051257269 title "High-Resolution Reservoir Modeling Using Image Fusion Technique in History Matching Problems" @default.
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• W2051257269 doi "https://doi.org/10.2118/164861-ms" @default.
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