One-thousand bimodal permeability realizations for research on representative model selection in subsurface flow problems.
Abstract/Contents
- Abstract
This data set contains the computational models for the paper on selecting representative models published in Computers & Geosciences.
Example_1_100x100_conditional_models.zip contains log-permeability realizations of size 100x100 with bimodal permeability distribution. These realizations are conditioned to hard data at well locations. For obtaining the (isotropic) permeability values, use k=exp(m).
Example_2_100x100_unconditional_models.zip contains log-permeability realizations of size 100x100 with bimodal permeability distribution. These realizations are unconditional (i.e., no hard data). For obtaining the (isotropic) permeability values, use k=exp(m).
Description
Type of resource | software, multimedia |
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Date created | 2016 |
Creators/Contributors
Author | Shirangi, M. G. |
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Subjects
Subject | Subsurface flow |
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Subject | Representative models |
Subject | Robust optimization |
Subject | Optimization under uncertainty |
Subject | Production optimization |
Subject | K-means clustering |
Subject | K-medoids |
Subject | Unsupervised learning |
Subject | Feature selection |
Subject | Well placement |
Subject | Model selection |
Genre | Dataset |
Bibliographic information
Related Publication | Shirangi, M. G., and Durlofsky, L. J. (2016). A general method to select representative models for decision making and optimization under uncertainty, Computers & Geosciences 96: 109-123. http://dx.doi.org/10.1016/j.cageo.2016.08.002 |
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Location | https://purl.stanford.edu/xd561kr7001 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
Preferred citation
- Preferred Citation
- Shirangi, M. G., & Durlofsky, L. J. (2016). A general method to select representative models for decision making and optimization under uncertainty, Computers & Geosciences 96: 109-123.
Collection
Stanford Research Data
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