Sensitivity Analysis of Filtersim and Histogram Reproduction
Abstract/Contents
- Abstract
- Multiple-point statistics (mps) algorithms generate reservoir models that aim at reproducing geological patterns taken from a training image. These algorithms were implemented first in the snesim code developed by Strebelle (2000) then in the filtersim code developed by Zhang et al. (2006). While snesim is limited to facies categorical simulation, filtersim can handle both continuous and categorical variables. Though these algorithms have shown success in providing good reproduction of geological patterns present in hydrocarbon reservoirs, they still need to be improved to integrate more complex geology. As there is no unique mps algorithm, each algorithm was fully tested to understand how to tune its parameters. This work presents such extensive sensitivity analysis. In petroleum applications, simulated reservoir models are used in flow simulations that are very sensitive to the connectivity of low or high permeability regions. So it is important to control the reproduction of such connectivity patterns that can be either barriers or channels to flow. A sensitivity analysis is performed on the main parameters of filtersim to provide some guidelines on how to use them. Based on this analysis, this thesis proposes a method to improve the reproduction of the training image histogram.
Description
| Type of resource | text |
|---|---|
| Date created | June 2007 |
Creators/Contributors
| Author | Dujardin, Bruno |
|---|---|
| Primary advisor | Journel, Andre |
| Degree granting institution | Stanford University, Department of Energy Resources Engineering |
Subjects
| Subject | School of Earth Energy & Environmental Sciences |
|---|---|
| Genre | Thesis |
Bibliographic information
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Preferred citation
- Preferred Citation
- Dujardin, Bruno. (2007). Sensitivity Analysis of Filtersim and Histogram Reproduction. Stanford Digital Repository. Available at: https://purl.stanford.edu/fx208qx5006
Collection
Master's Theses, Doerr School of Sustainability
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