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 GH saturation in the samples among the three methods. The CNN and SVM also exhibit sufficient performances under the restricted conditions, but require improvements to their reliability and overall prediction performance. Furthermore, the RF yields the lowest mean square error and highest correlation coefficient between the original and predicted datasets. Although the GH CT images aid in approximately understanding how fluids act in a GH sample, difficulties were encountered in accurately understanding the behavior of GH in a GH sample during the experiments owing to limited physical conditions. Therefore, the proposed saturation modeling method can aid in understanding the behavior of GH in a GH sample in real-time with the use of an appropriate machine learning method. Furthermore, highly accurate descriptions of each saturation, obtained from the proposed method, lead to an accurate resource evaluation and well-guided optimal depressurization for a target GH field production.

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.