Characterizing Trends in Metering Techniques for Wet Gas Measurement: A Critical Review

This review critically examines advancements, performance, and challenges in wet gas measurement technologies, emphasizing the need for innovation and adaptation in industrial applications. Key advancements include the integration of machine learning (ML) for real-time calibration, hybrid metering systems combining differential pressure, ultrasonic, gamma-ray, and optical sensors, and the development of high-sensitivity hardware for diverse operational scenarios. Performance evaluations demonstrate promising accuracy by ML algorithms and hybrid systems for gas flow rates (<2%). Despite these successes, significant challenges persist, including dependency on flow regime-specific calibrations, noise interference, environmental sensitivity, and limitations in high gas and low liquid volume fractions. Besides, a lack of standardized testing and validation hampers the widespread adoption of these technologies. To address these gaps, this review recommends advancements in adaptive ML algorithms, robust and noise-resistant sensor designs, and extensive field validation across diverse conditions. The exploration of computational techniques, such as digital twins, alongside sustainability-focused designs, further highlights pathways for improvement. Through a detailed analysis of industry applications, this review assesses the suitability of existing methods while proposing a roadmap for future research to enhance measurement reliability, scalability, and adaptability in complex multiphase flow environments.