Research on well group classification of polymer flooding based on reservoir data mining and parameter inversion

Because of the diverse depositional environment, development and oil genesis status of the targeted reservoir in polymer oil displacement, the physical parameters in polymer oil displacement block are remarkably diverse, so the enhanced oil recovery range between diverse well groups of polymer oil displacement is quite different, and it's hard to realize the tracking of the development, adjust and evaluate the later phase of polymer oil displacement.In order to establish the stage adjustment standard of polymer oil displacement, it's imperative to establish stage recovery normal curves for diverse kinds of polymer oil displacement wellgroups.Herein, the sensitivity of factors affecting well group classification was determined by analyzing static and kinetic data of polymer oil displacement block, and the method of well group categorization was realized via gray relation approach and statistics.On this basis, EnKF approach was utilized to realize the inversion of polymer flooding property parameters and phase permeation curves of diverse wellgroups. By combining EnKF approach with polymer oil displacement modeling technique, the development effects of diverse kinds of polymer oil displacement wellgroups were predicted, and the development effect criteria of diverse kinds of polymer oil displacement wellgroups were obtained.