Direct Conditioning of Upscaled Reservoir Models to Fine Scale Well Data Using Direct Sampling
Abstract/Contents
- Abstract
- A method for conditioning coarse-scale realizations constrained to fine-scale well data is presented based on the direct sampling technique. The workflow starts from a fine-scale training image that is upscaled to a coarse-scale training image using flow-based upscaling. This joint, multi-scale training image establishes a relationship between fine-scale values and coarse grid values and is used to calibrate a conditional density of coarse grid values conditioned to actual subsurface fine-scale data such as obtained from cores. These conditional densities, constructed at every coarse grid block that contains fine-scale data, represent the uncertainty due upscaling of fine-scale data. To account for this uncertainty, we generate multiple alternative coarse block conditioning data that can then be used in direct sampling to directly generate coarse-scale realizations accounting for the fine-scale data. We compare our method with the “perfect” technique, which requires generating fine-scale realizations and upscale all of them, and, with the “fast” method, which relies on direct generation of coarse-scale models conditioned to fixed coarse grid values at the well location. Our comparison is in terms of uncertainty quantification in recovery for an aquifer storage and recovery flow model.
Description
| Type of resource | text |
|---|---|
| Date created | June 2014 |
Creators/Contributors
| Author | Lowry, Brent |
|---|---|
| Primary advisor | Caers, Jef |
| 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
- Lowry, Brent. (2014). Direct Conditioning of Upscaled Reservoir Models to Fine Scale Well Data Using Direct Sampling. Stanford Digital Repository. Available at: https://purl.stanford.edu/mr991kt4275
Collection
Master's Theses, Doerr School of Sustainability
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- Contact
- brannerlibrary@stanford.edu
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