Segmentation of two-phase flow X-ray tomography images to determine contact angle using deep autoencoders

Abstract

This study improves the characterization of in situ contact angles in porous media by employing deep learning techniques (SegNet, UNet, ResNet, and UResNet) for multiphase segmentation of micro-CT images. The algorithms were tested on high-resolution X-ray images of a steady-state flow experiment where two fluid phases were simultaneously injected at different fractional flows. The models were trained to segment the images into solid, aqueous phase, and non-aqueous phase liquid (NAPL). The UResNet demonstrated the best performance with an f1-score of 0.966 for the test dataset. More importantly, the UResNet offered higher reliability than the watershed algorithm for various fractional flows based on visual inspection and phase distribution analysis. The porosity calculation error of the watershed method (7.8 %) was reduced to 5.1 % by UResNet. Furthermore, UResNet accurately depicted the consistently mixed-wet condition of the rock sample throughout the experiment, in contrast to the watershed segmentation that yielded inconsistencies in contact angle calculations at an aqueous phase fractional flow of 0.01.