Abstract Gas–liquid two-phase flow measurement in vertical annular pipes remains a challenge in petroleum engineering and related fields, due to the complex flow regimes and asymmetric cross-sectional geometry. In this study, a non-invasive soft-sensing method based on multi-scale differential pressure signals and machine learning is developed for flow rate prediction in vertical annular (inner/outer diameters 75/125 mm) counter-current gas–liquid two-phase flow.
