【国外硕士】Upscaling Errors in Reservoir Simulation

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Title:Upscaling Errors in Reservoir Simulation
Author:Ritesh Sablok

Year:2004

Degree:MS



Abstract:

Reservoir simulation models are used as an everyday tool for reservoir management. Number of grid blocks in a simulation model and number of components to describe the fluid in a compositional model are two parameters, which are regularly upscaled for building reasonably sized simulation models. This work attempts to understand the errors introduced due to these upscaling steps. Firstly, the generality of the purely local single phase upscaling is emphasized and the errors introduced due to their use in complex multiphase flows are investigated. Three kinds of errors, namely total upscaling errors, discretization errors, and errors due to the loss of heterogeneity are defined and their behavior as a function of the level of upscaling, is studied for different types of permeability distributions. The reasons for apparently low total upscaling errors at high levels of upscaling are investigated. The efficacy of the geostatistical tools (variograms, QQ plots), in understanding the geological structure of the upscaled models as compared to the reference model, is tested for different cases. Secondly, the uncertainty in flow results introduced due to the upscaling errors is studied in conjunction with the uncertainty incorporated through the introduction of geological variability in the ensemble of geological models. It is shown that the geological uncertainty captured by the ensemble of models produced by integrating data through geostatistical tools is lost at high levels of upscaling. Furthermore, upscaled models can be biased in terms of the expected production profile. Upscaling the detailed compositional description of a reservoir fluid to an EOS model with few pseudo-components calls for tuning of the EOS parameters through regression. Three regression procedures for tuning of EOS parameters are studied and their impact on the predictive capabilities of the resulting EOS models is investigated.

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