Abstract
Accurate measurement and control of wet and sour gas flow remain critical challenges in modern oil and gas production, transportation, and processing systems. In operating conditions, natural and associated gases rarely exist as single-phase media and commonly contain liquid components, primarily water and hydrocarbon condensates, forming multiphase mixtures. Even insignificant amounts of liquid significantly affect flow hydrodynamics and introduce substantial uncertainties in conventional gas flow measurement methods. This review analyzes the importance of flow rate measurement in binary gas-water systems and examines the evolution of wet gas metering technologies from traditional approaches toward intelligent measurement concepts. The study summarizes the physical characteristics of wet gas flows, including phase slip, flow regime variability, and the influence of liquid loading on measurement accuracy. The limitations of classical metering techniques are discussed from a system perspective, emphasizing that measurement errors originate mainly from the dynamic structure of multiphase flow rather than from individual sensor inaccuracies. Particular attention is given to operational challenges associated with sour gas environments, where corrosion, sensor degradation, and changing thermophysical properties further complicate reliable measurements. Based on the analyzed literature, current development trends indicate a transition from single-instrument solutions to hybrid and multisensory measurement systems capable of simultaneously estimating flow rate and phase composition. The review highlights emerging directions involving data-driven modeling, artificial intelligence, and digital twin technologies for real-time identification of flow conditions. These approaches enable adaptive measurement strategies that reduce dependence on empirical correlations and improve robustness under variable operating regimes. The review demonstrates that future wet gas metering systems will function as intelligent monitoring platforms integrated into digital oilfield infrastructures. The combination of multiphysical sensing, advanced signal processing, and machine learning provides a pathway toward reliable inline measurement without phase separation. The presented analysis outlines key technological challenges and identifies research directions necessary for developing next-generation intelligent measurement systems for complex multiphase gas flows.