Using machine vision algorithms for characterizing gas-liquid slug flows in vertical pipes

Abstract

Slug flow, characterized by the distinctive interfacial structures of Taylor bubbles surrounded by liquid films and bridged by aerated liquid slugs, is a dynamically complex two-phase flow pattern exist in many oil and gas, and energy systems. Accurate and precise quantification of such complex flow behaviour is essential for optimal design, safe operation, and reliable modelling of these systems. Existing image-based measurement techniques mostly rely on offline image processing algorithms and are often limited to a narrow set of flow characteristics primarily focusing on Taylor bubbles. Such constraints not only impede real-time flow monitoring and regulation but also leave liquid slug characteristics unmeasured, resulting in an inability to accurately determine the flow characteristics and extract instantaneous void fraction signals. Present study examined the performance of adaptive thresholding (AT) and background subtraction (BS) algorithms in capturing slug flow characteristics. It was found that while the former excels in Taylor bubbles detection and the latter in small bubbles identification, neither individually addresses the accurate measurement of both flow structures' characteristics. This observation, along with the mentioned restrictions of existing algorithms are the main reason for developing the present combined machine vision-based algorithm. While unlocking the ability to extract instantaneous void fraction signals, this new approach facilitates online measurement of a wide range of key flow characteristics, including Taylor bubble length, velocity, void fraction, and surrounding liquid film thickness; liquid slug length and void fraction; and slug unit length, void fraction, and frequency. Parallel to the high-speed imaging, time-series void fraction data was collected using two capacitance sensors installed alongside the imaging area on the pipe, providing benchmark data essential for the validation of the new algorithm's accuracy. The comparisons demonstrated a high degree of accuracy and precision for the combined algorithm. Quantitatively, the new algorithm measured key unit cell characteristics with RMS errors ranging from 2 to 10 %, while the BS and AT algorithms exhibited wider RMS error ranges of 8–46 % and 2–53 %, respectively. This underscores the new algorithm's potential as a transformative tool for slug flow analysis.