Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/345811
Type: Artigo
Title: A new approach with multiple realizations for image perturbation using co-simulation and probability perturbation method
Author: Oliveira, Gonçalo Soares
Maschio, Célio
Schiozer, Denis José
Abstract: History matching is an inverse problem with multiple possible answers. The petrophysical properties of a reservoir are highly uncertain because data points are scarce and widely scattered. Some methods reduce uncertainty in petrophysical characterization; however, they commonly use a single matched model as a reference, which may excessively reduce uncertainty. Choosing a single image may cause the model to converge to a local minimum, yielding less reliable history matching. This work improves on the history matching presented by Oliveira et al. ((2017a) J. Petrol. Sci. Eng. 153, 111–122) using a benchmark model (UNISIM-I-H based on the Namorado field in Brazil). We use a new approach for a Probability Perturbation Method and image perturbation using Co-Simulation. Instead of using a single image as the reference, a set of best images is used to increase variability in the properties of the reservoir model while matching production data with history data. This approach mitigates the risk of the potentially excessive reduction of uncertainties that can happen when using a single model. Our methodology also introduces a new objective function for water breakthrough, improving model quality because of the importance of matching the water breakthrough in the process. Our proposed methodology for image perturbation uses the UNISIM-I-H, which comprises 25 wells and has 11 years of history data. Our methodology made the process of calibration more effective than the history matching by Oliveira et al. ((2017a) J. Petrol. Sci. Eng. 153, 111–122). Cross-influence was minimized, making the history matching more objective and efficient, and consequently, the production forecasts more reliable
Subject: Probabilidades
Country: França
Editor: EDP Sciences
Rights: Aberto
Identifier DOI: 10.2516/ogst/2018065
Address: https://ogst.ifpenergiesnouvelles.fr/articles/ogst/abs/2018/01/ogst170218/ogst170218.html
Date Issue: 2018
Appears in Collections:FEM - Artigos e Outros Documentos

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