Direct Measurement of Pore-Scale Contact Angle and Curvature from Grayscale Micro-CT Images

Abstract Fluid flow in porous media is central to applications such as hydrocarbon recovery and CO₂ sequestration, yet accurately quantifying capillary pressure and wettability remains challenging. Traditional imaging techniques rely on segmented grayscale images, a process that can introduce artifacts and misrepresent the true interfaces between the fluids and solid. In this work, we present an approach that combines conventional segmentation with direct analysis of grayscale gradient changes to enhance the detection of fluid–fluid and solid–fluid interfaces. Using high-resolution X-ray micro-computed tomography on Ketton limestone samples—with brine and doped decane mimicking real-world conditions—we demonstrate that leveraging natural intensity variations in the grayscale images improves the accuracy of interface delineation. Our experimental results show that the standard segmentation method estimates a capillary pressure of 0.436 kPa, accompanied by a skewed curvature distribution, whereas our enhanced approach produces a more normally distributed curvature and a reduced capillary pressure estimate of 0.217 kPa. Both methods confirm a strong water-wet condition with contact angles between 31° and 33°, but the enhanced method yields a more consistent and reliable contact angle distribution. By directly incorporating grayscale gradient information, our method reduces the errors typically introduced during segmentation and provides a clearer picture of the pore-scale interfacial geometry. This improvement is crucial for reliable pore-by-pore analysis and better prediction of fluid behavior in complex porous systems. The approach offers a practical and robust framework that may be further refined with automated edge detection techniques in future work. Overall, our results suggest that integrating grayscale analysis into existing imaging workflows can significantly enhance the precision of interfacial property measurements, thereby supporting more informed decision-making in energy and environmental applications.