One-thousand bimodal permeability realizations for research on representative model selection in subsurface flow problems.

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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).

Type of resource	software, multimedia
Date created	2016

Author	Shirangi, M. G.

Subject	Subsurface flow
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

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
Location	https://purl.stanford.edu/xd561kr7001

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: 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.

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