Data-Driven Three-Phase Saturation Identification from X-ray CT Images with Critical Gas Hydrate Saturation

Data-Driven Three-Phase Saturation Identification from X-ray CT Images with Critical Gas Hydrate Saturation

This study proposes three-phase saturation identification using X-ray computerized tomography (CT) images of gas hydrate (GH) experiments considering critical GH saturation (S<sub>GH,C</sub>) based on the machine-learning method of random forest. Eight GH samples were categorized into th...

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Bibliographic Details
Main Authors: Sungil Kim, Kyungbook Lee, Minhui Lee, Taewoong Ahn
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Energies
Subjects:
X-ray CT image
critical gas hydrate saturation
saturation identification
random forest
data management
machine-learning
Online Access:https://www.mdpi.com/1996-1073/13/21/5844

Internet

https://www.mdpi.com/1996-1073/13/21/5844

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