Abstract
The contact angle is a crucial parameter for assessing wettability, but traditional methods like Amott and USBM provide different metrics. To enhance our understanding of wettability and enable quantitative characterization, in-situ contact angle measurement using X-ray micro-computed tomography has emerged as a valuable tool in the past decade. This technique allows for the precise determination and analysis of contact angles within porous media under various conditions.
Published strategies for determining in-situ contact angles (or distributions of angles) on pore walls in porous media can mainly be divided into four groups:
1. Direct geometric determination of local contact angles along identified three-phase contact lines.
2. Direct geometric determination of local contact angles based on image recognition/machine learning.
3. Determination of average contact angles per continuous fluid domain based on the domain's topology.
4. Determination of an equivalent thermodynamically consistent contact angle for the entire medium based on energy conservation during the formation of phase interfaces.
This study focuses on developing a robust method for determining contact angles within porous media. We explore two primary approaches: directly measuring the angle at three-phase contact lines and using image recognition/machine learning. Our method involves a regression-based algorithm that additionally assigns a confidence value to each calculated angle, enabling us to assess the reliability of the results. Results are compared with several established approaches in the literature. Through numerical simulations and application to experimental data from both synthetic and real porous media, it is shown that the proposed method is a more reliable and efficient approach for determining contact angles and its distribution within porous media at in-situ conditions.
Published strategies for determining in-situ contact angles (or distributions of angles) on pore walls in porous media can mainly be divided into four groups:
1. Direct geometric determination of local contact angles along identified three-phase contact lines.
2. Direct geometric determination of local contact angles based on image recognition/machine learning.
3. Determination of average contact angles per continuous fluid domain based on the domain's topology.
4. Determination of an equivalent thermodynamically consistent contact angle for the entire medium based on energy conservation during the formation of phase interfaces.
This study focuses on developing a robust method for determining contact angles within porous media. We explore two primary approaches: directly measuring the angle at three-phase contact lines and using image recognition/machine learning. Our method involves a regression-based algorithm that additionally assigns a confidence value to each calculated angle, enabling us to assess the reliability of the results. Results are compared with several established approaches in the literature. Through numerical simulations and application to experimental data from both synthetic and real porous media, it is shown that the proposed method is a more reliable and efficient approach for determining contact angles and its distribution within porous media at in-situ conditions.
