Transformation Spaces for Determining Spatial Model Complexity
Abstract/Contents
- Abstract
- How complex should a spatial or spatial-temporal geostatistical model be in order to suit the purpose for which it is used? This is a common question to all applications of geostatistical modeling whether it is mining, petroleum, environmental or any other. How many grid-blocks, how many indicator categories should we use? Surprisingly very few general and flexible tools are available to start addressing this important question. In general, complex models are favored over simple ones. CPU intensive task such as uncertainty quantification forces the reservoir engineer to simplify the preexisting models. The degree of simplification is subjective and made with trial and error type of procedures. We propose a general framework for determining a suitable spatial model complexity on the basis of distances obtained from a series of image transformations and the linear combinations of these distances thereof. Model complexity is not evaluated at a single model basis, rather it is considered for a set of models. This general framework is applied in two workflows, namely Top-down and Bottom-Up to reach a simple enough set of models and different capabilities of the workflows are demonstrated in illustrative cases.
Description
| Type of resource | text |
|---|---|
| Date created | June 2012 |
Creators/Contributors
| Author | Aydin, Orhun |
|---|---|
| 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
- Aydin, Orhun. (2012). Transformation Spaces for Determining Spatial Model Complexity. Stanford Digital Repository. Available at: https://purl.stanford.edu/bg016hs1268
Collection
Master's Theses, Doerr School of Sustainability
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- brannerlibrary@stanford.edu
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