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Jr, J. D. Lira; Willmersdorf, R. B.; Horowitz, B.; Afonso, S. M. B.;

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This paper presents a computational methodology for dynamic allocation of the rates in the production and injection wells, considering uncertainties related to the petrophysical properties such as permeability. The permeability field is considered as a stochastic field, featuring uncertainty as an input variable of the model. The stochastic input fields are described with the Karhunen-Loève expansion, and the stochastic responses of interest are expressed with polynomial chaos expansion. In this work surrogate models are used, to reduce the computational cost of the whole process. The surrogate models are used together with the strategy named sequential approximation optimization (SAO). The layered and nested methodology is used to perform optimization under uncertainties. Waterflooding case studies on a model reservoir are shown. The optimization cases of dynamic allocation of production rates shows that the methodologies presented here have achieved robust results.

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Palavras-chave: Oil Reservoir Engineering, Waterflooding, Optimization under Uncertainty.,


DOI: 10.5151/meceng-wccm2012-18732

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Como citar:

Jr, J. D. Lira; Willmersdorf, R. B.; Horowitz, B.; Afonso, S. M. B.; "WATERFLOODING OPTIMIZATION UNDER UNCERTAINTY", p. 2138-2151 . In: In Proceedings of the 10th World Congress on Computational Mechanics [= Blucher Mechanical Engineering Proceedings, v. 1, n. 1]. São Paulo: Blucher, 2014.
ISSN 2358-0828, DOI 10.5151/meceng-wccm2012-18732

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