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Title: Model Selection for Monitoring CO2 Plume during Sequestration

The model selection method developed as part of this project mainly includes four steps: (1) assessing the connectivity/dynamic characteristics of a large prior ensemble of models, (2) model clustering using multidimensional scaling coupled with k-mean clustering, (3) model selection using the Bayes' rule in the reduced model space, (4) model expansion using iterative resampling of the posterior models. The fourth step expresses one of the advantages of the method: it provides a built-in means of quantifying the uncertainty in predictions made with the selected models. In our application to plume monitoring, by expanding the posterior space of models, the final ensemble of representations of geological model can be used to assess the uncertainty in predicting the future displacement of the CO2 plume. The software implementation of this approach is attached here.
Publication Date:
OSTI Identifier:
Report Number(s):
UTGS; 003350IBMPC00
DOE Contract Number:
Resource Type:
Software Revision:
Software Package Number:
Software Package Contents:
Open Source Software package available from University of Texas at Austin at the following URL:
Software CPU:
Open Source:
Source Code Available:
Research Org:
University of Texas at Austin
Sponsoring Org:
Contributing Orgs:
Sanjay Srinivasan Steven Bryant Department of Petroleum & Geosystems Eng. University of Texas at Austin
Country of Publication:
United States

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