Meshaal Ahmed Al-Rashid

Accurate flow rate estimation is critical for optimising gas production and enabling proactive reservoir management. At QatarEnergy LNG, Conventional metering technologies such as wet gas meters, multiphase flow meters (MPFMs), and test separators are commonly used for surveillance and allocating the rate of the separate phases. However, these systems present limitations in terms of cost, scalability, and operational reliability, specifically under dynamic flow conditions in wet gas wells with varying production characteristics. Test separators – though are widely considered as the reference method - are often constrained by infrastructure availability, limited test frequency and operational accessibility leading to a reduction in the effectiveness of capturing timely gas, water, and condensate rates. This paper presents the deployment and evaluation of Virtual Flow Metering (VFM) as a scalable, nonintrusive alternative to traditional metering systems. By leveraging real-time surface data with physics-based and data-driven models, VFMs offers continuous estimation of gas, condensate, and water rates at the wellheads. While the use of test separators remains essential for initial model calibration and periodic validation, VFMs can significantly reduce the need for frequent physical testing and improve surveillance insights across the asset. Benchmarking results from QatarEnergy LNG field applications show that VFMs can deliver accurate and reliable estimates across wells with variable and transient flow behaviour. The findings support VFMs as a key enabler of digital well surveillance, offering a cost-effective and operationally efficient tool for production optimisation. Looking ahead, integrating artificial intelligence (AI) into the VFMs systems presents a promising opportunity to further enhance adaptability, accuracy and predictive capabilities. This evolution positions VFMs as a foundation element in the future of autonomous production monitoring and reservoir management, contributing to QatarEnergy LNG broader objectives in digital transformation of the subsurface operations.