Evaluation of saturation changes during gas hydrate dissociation core experiment using deep learning with data augmentation

Abstract

This study proposes a reliable evaluation method for three-phase saturation (water, gas hydrate (GH), and gas) evaluation during the GH dissociation core experiment using deep learning. A convolutional neural network (CNN) takes computed tomography (CT) images obtained during the GH core experiment as an input and provides three-phase saturation as an output. Although machine/deep learning methods have been applied to the saturation evaluation from CT images in previous research, they were not reliable due to the lack of adequate amount of training data where the model could not find appropriate parameters. Besides, non-zero gas hydrate saturation showed where it was supposed to be zero. This study improved the evaluation of three-phase saturation and solved the non-zero GH saturation problem by acquirement of extra data and application of data augmentation with CNN. The results of CNN and CNN with data augmentation presented 34% and 29% error compared to those of random forest. CNN with data augmentation brought 85% and 44% of error and its variance compared to those of CNN without data augmentation, respectively. Consequently, based on domain knowledge for GH, when it comes to the robustness of random data composition and consistency of performance, the evaluation of three-phase saturation can be boosted using CNN with data augmentation. • Machine learning was used to evaluate saturations of gas hydrate core experiment. • Adequate training data brought a successful learning performance of CNN. • Improved evaluation method using CNN for three-phase saturations was proposed. • Data augmentation can improve reliability and robustness of CNN performance. • Data augmentation is expected to be a solution for data shortage and overfitting.