Using Unsupervised Machine Learning to Characterize Capillary Flow and Residual Trapping

Recent research has highlighted the impact mesoscale heterogeneity can have on larger‐scale multiphase fluid flow properties. However, currently, there is no consensus on how to quickly and reliably analyze coreflooding experimental data to gain insights into mesoscale capillary‐dominated flow behav...

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Veröffentlicht in:	Water resources research 2020-08, Vol.56 (8), p.n/a, Article 2020
Hauptverfasser:	Ni, Hailun, Benson, Sally M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Capillary flow Carbon dioxide ccoreflooding experiments Clustering CO2 sequestration Computational fluid dynamics Environmental Sciences Environmental Sciences & Ecology Experimental data Fluid flow Heterogeneity Hypothesis testing Learning algorithms Life Sciences & Biomedicine Limnology Machine learning Marine & Freshwater Biology Mesoscale phenomena multiphase flow Permeability Physical Sciences Physics Properties Residual gas residual trapping Sandstone Saturation Science & Technology Sedimentary rocks Time series time series clustering Trapping Unsupervised learning unsupervised machine learning Water Resources
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