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 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.