Virtual Multiphase Flow Meter using combination of Ensemble Learning and first principle physics based

Abstract This paper describes a Virtual Flow Meter (VFM) to estimate oil, gas and water flow rate by combining two distinct approaches i.e., data-driven Ensemble Learning algorithm and first principle physics-based transient multiphase flow simulator. The VFM uses a common real-time sensor readings and the estimated flow rates were then combined using a new combiner approach which provides confidence decay and historical performance factors to assign confidence and contribution weights to the base estimators, and then aggregates their estimates to deliver more accurate flow rate estimates. This technique was tested for over 6 months at an offshore oil facility having two oil wells. The technique successfully delivered a 50% improvement in measurement performance compared to stand-alone VFMs. This combiner technique will be of great benefit to surveillance engineers by providing additional real-time production monitoring in addition to acting as a verification tool for physical multiphase flow meters (MPFMs).