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

• 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

This study proposes three-phase saturation identification using X-ray computerized tomography (CT) images of gas hydrate (GH) experiments considering critical GH saturation (S_GH,C) based on the machine-learning method of random forest. Eight GH samples were categorized into three low and five high GH saturation (S_GH) groups. Mean square error of test results in the low and the high groups showed decreases of 37% and 33%, respectively, compared to that of the total eight. Additionally, a universal test set was configured from the total eight and tested with two trained machines for the low and high GH groups. Results revealed a boundary at ~50% of S_GH signifying different saturation identification performance and the ~50% was estimated as S_GH,C in this study. The trained machines for the low and high S_GH groups had less performance on the larger and smaller values, respectively, of S_GH,C. These findings conclude that we can take advantage of suitable separation of obtained training data, such as GH CT images, under the criteria of S_GH,C. Moreover, the proposed data-driven method not only serves as a saturation identification method for GH samples in real time, but also provides a guideline to make decisions for data acquirement priorities.