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- W2077150726 abstract "Summary This paper presents a new geostatistical approach, called the general-inversion method that incorporates dynamic data (pressure transient, well production, water cut) into a reservoir model. It essentially involves two basic tools: the fast-Fourier-transform moving-average (FFT-MA) generator and the gradual-deformation method. The FFT-MA generator produces unconditional Gaussian-related fields that can be perturbed on the basis of the gradual-deformation principles. Thus, the initial structure of the realizations is preserved. Integrating these two primary algorithms within an optimization process yields the general-inversion procedure. The method has at least three significant advantages. First, unlike the traditional inverse approaches, the general-inversion method easily accounts for the constraint relative to prior geostatistical information. Second, the permeability/porosity distribution can be perturbed globally or locally. And third, the method makes it possible to condition the permeability/porosity distribution and the structural (or geostatistical) parameters (mean, variance, correlation length) at the same time. Because of these distinct capabilities, the suggested inversion scheme can qualify as a general one. The suggested approach was applied to synthetic examples designed to address such key issues as characterization of permeability distribution, updating of a reservoir model to integrate newly obtained data, and estimation of correlation length from well data." @default.
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- W2077150726 date "2001-03-01" @default.
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- W2077150726 title "Stochastic Reservoir Modeling Constrained to Dynamic Data: Local Calibration and Inference of Structural Parameters" @default.
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- W2077150726 doi "https://doi.org/10.2118/68883-pa" @default.
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