Data-driven estimation of three-phase saturation during gas hydrate depressurization using CT images

Abstract

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. • Random forest (RF) is successfully applied to estimate the three-phase saturation from gas hydrate (GH) core X-ray CT images. • More than 4,000 CT images during GH experiment and their water, gas, and GH saturations are used to train and test RF model. • The predicted saturations from the trained RF show reliable trend of GH dissociation during core depressurization experiment. • The new training data in the depressurization stage enhance the estimation performance of RF.