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- W1971825375 abstract "A key problem in Reservoir Characterization concerns the description and visualization of reservoir heterogeneities as represented by properties such as porosity, permeability, etc. The inherent nonuniqueness associated with this problem has prompted considerable interest in the development and application of Stochastic Imaging techniques. Such techniques are designed to generate a family of equiprobable images of these properties - each image being consistent with all the available quantitative (well-logs, cores, seismic derived constraint intervals, well-test information) and qualitative (geological interpretations) information, and its spatial correlation characteristics. These stochastic images may be viewed as samples from approximations to the optimal Bayesian posterior distribution on the unknown properties. Statistical analysis of the spread of this distribution allows for the quantification of risk/uncertainty associated with the spatial variability of these properties. Such distributions can be used in dynamic flow simulations and statistical decision theoretic techniques for optimal forecasting and management of the reservoir." @default.
- W1971825375 created "2016-06-24" @default.
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- W1971825375 date "1994-01-01" @default.
- W1971825375 modified "2023-09-26" @default.
- W1971825375 title "Stochastic imaging of reservoir properties using probalistic neural networks" @default.
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- W1971825375 doi "https://doi.org/10.3997/2214-4609.201409905" @default.
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