Title:

Multiresolution Reparameterization and Partitioning of Model Space for Reservoir Characterization

Author:

Isha Sahni

Year:

2006

Degree:

PhD

Adviser:

Horne

File Size:

4.4MB

View File:

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Access Count:

358

Abstract:

This work develops a generalized wavelet-based methodology for stochastic data integration in complex reservoirs models. This is an extension of our earlier work for simpler reservoir descriptions. A single history-matched reservoir permeability model is combined with a stochastic geological description to obtain multiple equiprobable reservoir descriptions using wavelet transforms of the parameter distribution (permeability). The algorithm has been extended and generalized to be usable with commercial reservoir simulation software and to enable handling of three-dimensional models and production scenarios. We also conducted a study of sensitivity coefficient distributions, thresholding and averaging techniques, and a comparison of different Haar wavelet implementations.

Wavelet coefficients of reservoir parameter distributions can, to some extent, be parti- tioned into sets of history-matching and geologic coefficients and modified independently. Inverse transformation of these coefficients yields multiple reservoir model results, all of them matched to history. A significant reduction in time can obtained for stochastic mod- eling of reservoirs by the decoupling of production data and other parameters, since only a single history match is required.

Thus the proposed algorithm addresses the issue of stochastic modeling of complex reservoirs by integrating all available sources of information. From a single history-matched model we obtain a set of distinct equiprobable reservoir models that can then be used to evaluate uncertainty and make future production predictions and reservoir management decisions.


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Copyright 2006, Isha Sahni: Please note that the reports and theses are copyright to their original authors. Authors have given written permission for their work to be made available here. Readers who download reports from this site should honor the copyright of the original authors and may not copy or distribute the work further without the permission of the author, Isha Sahni.

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