The installation of intelligent wells to improve the economics of production is now common practice. These wells allow the access to marginal reservoirs, for which dedicated production might not be economic, and also accelerate the recovery. Sensors, flow-control and other devices can be used to manage the production from the commingled reservoirs and optimize the recovery.Traditional methods for production optimization and back-allocation of complex well configurations, such as nodal analysis, work only for a static problem. They cannot account for the dynamic changes that occur in time in the connected system of reservoirs and wellbore. Once multiphase flow occurs, both the change of the fluid mobility in the reservoir and the change of the choke performance cannot be correctly addressed. Moreover, the large number of uncertainties from reservoir to wellbore behavior that influence the performance of those advanced wells cannot be accurately dealt with using traditional approaches.A process is introduced that creates the most accurate well model of an intelligent completion accounting for all effects influencing the pressure behavior in the wellbore and in the reservoir. This model is used for optimization over all static and dynamic uncertainties to derive an interaction strategy with the intelligent well that maximizes oil production. Furthermore, the back-allocation algorithm is calibrated and trained on the proxy model of the well model.
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