Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning
Author:
Funder
DOE | Laboratory Directed Research and Development
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
http://www.nature.com/articles/s41598-018-30117-1.pdf
Reference33 articles.
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3. Carrigan, C. R., Heinle, R. A., Hudson, G. B., Nitao, J. J. & Zucca, J. J. Trace gas emissions on geological faults as indicators of underground nuclear testing. Nature 382, 528–531 (1996).
4. Pyrak-Nolte, L. J. & Nolte, D. D. Approaching a universal scaling relationship between fracture stiffness and fluid flow, Nat. Commun. https://doi.org/10.1038/ncomms10663 (2015).
5. Hyman, J. D. et al. dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport. Comput. Geosci. 84, 10–19 (2015).
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