Gradient Information Enhanced Image Segmentation and Automatic In Situ Contact Angle Measurement Applied to Images of Multiphase Flow in Porous Media

A gradient-information-enhanced image segmentation method using convolutional neural networks is presented, and then combined with contact angle measurement to establish an automated processing workflow. For three-dimensional X-ray images, the segmentation accuracy at interfaces and sparsely distributed small objects directly influences the accuracy of the contact angle measurement. Leveraging reliable gradient information to train the neural network, this segmentation method addresses the issue of inaccurate segmentation of interfaces even at low resolution and with small objects present. Furthermore, memory requirements are reduced by performing analysis on orthogonal two-dimensional planes. The workflow was tested on water-wet Ketton limestone, as well as on both water-wet and mixed-wet sandstone and a reservoir carbonate. The results from both the segmentation and contact angle measurements underscore the effectiveness of the approach. Notably, the workflow shows considerable generalizability and robustness, even with varying wettability and lithology.