Agradecimentos: The authors are grateful to PETROBRAS, the research network SIGER 3 (Grant Agreement No. 0050.0100204.16.9), the National Agency of Petroleum, Natural Gas and Biofuels (ANP), the Center of Petroleum Studies (CEPETRO-UNICAMP) particularly the UNISIM research group, the Department of...

Agradecimentos: The authors are grateful to PETROBRAS, the research network SIGER 3 (Grant Agreement No. 0050.0100204.16.9), the National Agency of Petroleum, Natural Gas and Biofuels (ANP), the Center of Petroleum Studies (CEPETRO-UNICAMP) particularly the UNISIM research group, the Department of Energy of the School of Mechanical Engineering of the University of Campinas (DE-FEM-UNICAMP), the Foundation CMG and National Council for Scientific and Technological Development (CNPq), for supporting this research, and also, to the Computer Modelling Group (CMG) and Schlumberger for software licenses

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...

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

CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ

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