Intelligent well completion has been used in various applications including but not limited to fields with multiple reservoirs. In such applications, estimation and allocation of downhole flow rates at each reservoir are critical for efficient reservoir management. One way of estimating downhole flow rates is the deployment of dedicated physical zonal flowmeters, using virtual flowmeter techniques based on the architecture of the intelligent well completion or combination of both options. This paper describes the methodology and field wide application of using real time data from an intelligent well to estimate flow rates without the need for a physical flow meter. The real time data are from the installed downhole gauges in the well. This is combined with interval control valves, static and dynamic well information to provide reliable estimate of well production rate. Since downhole pressure, temperature and ICV information is already available in an intelligent well, this technique provides a lower cost option of obtaining zonal and total well production and injection rates. The methodology used incorporates analytical choke equations, tubing performances, and nodal analysis (inflow performance relationship) with other reservoir parameters to build a flow estimation algorithm and model. Various downhole equipment (interval control valves, packers, pressure and temperature sensors etc) and related well information are captured into the system to set initial and final boundary conditions. Well test data can be used to calibrate the system and improve the accuracy of the model. In the field application described, results vary from well to well with field average estimates within +/−10% when compared to measurements from normal surface metering systems. Well tests from the surface measurements were used to calibrate and improve accuracy. The result shows an operating envelope that covers a range of pressure drop across the ICV. The method is capable of handling single and two phase system. Further enhancements are been made to handle multiphase and systems outside the steady state regime. In addition, enhanced data filtering techniques implemented in the system help managed noisy data. The analytical techniques described enhance digital oilfield capability in optimizing production through affordable flow rate estimation for intelligent wells. The technique presented can also be used to increase the reliability of applicable wells since no additional physical hardware is required.