Applying AI-assisted image processing to detect and measure CO2 microbubbles for carbon storage

Abstract

To address the challenges in the CO2 injection process, CO2 microbubble dispersion has been proposed as an alternative to traditional methods, such as miscible injection and water-alternating-gas (WAG) injection. This study presents an AI-assisted model for detecting CO2 microbubbles, powered by the YOLOv8 algorithm, renowned for its high-accuracy predictions. Conventional image processing techniques often struggle with detecting microbubbles, particularly in cases involving overlapping bubbles, variations in size, and low-contrast images, which can lead to inaccuracies in bubble identification and measurement. In contrast, YOLOv8’s advanced detection capabilities offer a more robust solution by precisely localizing and classifying microbubbles, even in challenging scenarios. The model’s performance was rigorously evaluated, demonstrating its effectiveness as a valuable tool for microbubble analysis. The detection images processed using YOLOv8 illustrate its ability to accurately detect and classify bubbles of varying sizes, generating precise bounding boxes around each identified bubble. This combination of data visualization and advanced detection techniques underscores the efficacy of YOLOv8 in microbubble analysis, enabling accurate measurement and detailed characterization of bubble size distributions — an essential factor in optimizing chemical engineering processes.