Saturation Modeling of Gas Hydrate Using Machine Learning with X-Ray CT Images

Saturation Modeling of Gas Hydrate Using Machine Learning with X-Ray CT Images

This study conducts saturation modeling in a gas hydrate (GH) sand sample with X-ray CT images using the following machine learning algorithms: random forest (RF), convolutional neural network (CNN), and support vector machine (SVM). The RF yields the best prediction performance for water, gas, and...

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Bibliographic Details
Main Authors: Sungil Kim, Kyungbook Lee, Minhui Lee, Taewoong Ahn, Jaehyoung Lee, Hwasoo Suk, Fulong Ning
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Energies
Subjects:
X-ray CT image
gas hydrate sand sample
saturation modeling
machine learning
random forest
Online Access:https://www.mdpi.com/1996-1073/13/19/5032

Internet

https://www.mdpi.com/1996-1073/13/19/5032

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