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This study proposes three-phase saturation identification using X-ray computerized tomography (CT) images of gas hydrate (GH) experiments considering critical GH saturation (SGH,C) based on the machine-learning method of random forest. Eight GH samples were categorized into three low and five high GH saturation (SGH) 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 SGH signifying different saturation identification performance and the ~50% was estimated as SGH,C in this study. The trained machines for the low and high SGH groups had less performance on the larger and smaller values, respectively, of SGH,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 SGH,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.

The purpose of this study was to determine the three-phase saturation (water; gas hydrate, GH; and gas) in real time to gain insights into the GH depressurization process. Although X-ray computed tomography (CT) can be used to investigate the density changes in the GH core during the depressurization experiment, it is hard to distinguish between water and GH due to their similar densities and the limited resolution of the CT image. To address this issue, random forest (RF) was applied to quantitatively predict the three-phase saturation: CT images were used as input data and the three-phase saturation was the output. In the previous research, a RF model was developed based on training data that only involved the five preliminary stages before the depressurization step. However, the previous RF model failed to estimate the saturation values of the CT images during the depressurization. It could not properly estimate general GH dissociation trend and the GH equilibrium pressure, neither. In this study, CT data obtained in the early and late depressurization stage were used for the training of the RF model. The proposed method can be used to reliably estimate the GH dissociation behavior. The trained RF in this study can identify the GH equilibrium at which the dissociation starts (~16 MPa), which is consistent with the theoretical dissociation at a temperature and salinity of 16 °C and 3 wt%, respectively. The reliability of the proposed method was tested by checking if the estimated results approached zero GH saturation at the end of the GH dissociation (6 and 0 MPa). Therefore, the originality of this study is to improve RF model to reliably predict the three-phase saturation in real time during the GH depressurization by properly utilizing CT images in the depressurization stage for training RF. Based on further improvement, the proposed method could be utilized in the future to help finding adequate depressurization velocity by reliable saturation modeling and analysis of GH experiment because GH productivity depends on depressurization gradient.

Numerical estimation of physical properties from digital pore microstructures has drawn great attention and is being used for quantifying interrelation between various physical properties. The pore microstructures are commonly obtained by the X-ray microtomographic technique, which can give fairly accurate pore geometry. However, there is minor distortion due to the limited resolution or smoothing. This distortion can cause errors in estimating physical properties by pore-scale simulation techniques. Among the properties, seismic velocity would have relatively large errors since a small amount of change in grain contacts can cause significant over-estimation. In this paper, we analyzed the errors in seismic velocity by resolution and smoothing of pore geometry using three samples: an unconsolidated sand pack and two medium-porosity sandstones with different degrees of consolidation. As the resolution becomes poor, the calculated velocity increases linearly, while smoothing gives nonlinear trends; higher errors in the early stage of smoothing. As we expected, soft rocks have higher sensitivity, since the grain contacts are small and are sensitive to minor distortion. Within similar ranges, the resolution causes larger errors than smoothing. In addition, smoothing does not cause velocity over-estimation once the resolution becomes poor, while the resolution can create considerable errors in velocity even after significant smoothing. We conclude that the resolution should be considered in the first place when obtaining digital pore microstructures to minimize errors in velocity estimation. We can also suggest that a good care should be taken when applying smoothing filters, if a sample is suspected to be poorly-consolidated or to have high porosity.

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.