Fields with multiple producing reservoir units offer some of the most interesting asset development challenges. Several of these fields are now being completed with intelligent wells that enable commingling of multiple units with reservoir-control functionality. Unit production estimation and allocation of downhole flow rates are critical components of efficient reservoir and asset management. Deploying zonally dedicated subsurface flow meters is the most common way of estimating unit production rates on a real-time basis from an intelligent well. This paper presents results from analytical techniques for estimating unit production rates in real time by combining well architecture information, downhole pressure, and temperature data with appropriate reservoir inflow and interval control valve (ICV) flow equations. Since downhole pressure, temperature, and ICV information is already available in an intelligent well, this technique provides a lower-cost option for obtaining zonal and total well production and injection rates. The methodology used incorporates analytical choke equations, tubing performances, and nodal analysis (inflow performance relationships) with other reservoir parameters to build a flow estimation algorithm and model. Various downhole equipment (interval control valves, packers, pressure and temperature sensors, etc.) as well as related well information are brought 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. Field data from several wells have been run through the model; well tests from the field were used to calibrate and improve accuracy. Results vary from well to well. The system delivers flow-rate estimates greater than 90-percent accuracy when compared to actual flow-rate measurements from well tests and flow meters for some of the wells. The results show an operating envelope that covers a range of pressure drops across the ICV. Several considerations are being made to improve the results, especially outside the steady-state regime. The enhanced data filtering techniques implemented in the system helped manage "noisy" data. The analytical techniques described enhance digital capability in optimizing oilfield production through affordable flow-rate estimation for intelligent wells.
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