Water flooding performance prediction in layered reservoirs using improved capacitance-resistive model

Traditional strategies have attempted to combine complicated petrophysics, geophysics and thermodynamics with economic factors in order to determine the best method of recovery from a hydrocarbon reservoir. Traditional commercial simulators have been widely used with a long run time to solve and optimize this problem. A rapid initial estimation by less data is necessary for management of the field and problem optimization. This leads to development of fast simulation methods.The capacitance-resistive model (CRM) approach is a promising rapid evaluator of reservoir performance, which has been recently used for reservoir simulation. Injection and production rates in this model are considered as input or output signals for the reservoir model. Connections between the wells and the influences of injections on production rates are then calculated based on these signals to develop a simple model for the reservoir. This technique has been applied to a single layer reservoir to predict reservoir performance.In this paper, we extend the available capacitance-resistive approach to estimate and optimize waterflooding performance in layered reservoir. For this approach well injection/production data and, likewise, Production Logging Tools (PLT) should be coupled with the basic equations of the CRM. This new approach is applied for a synthetic waterflooded layered reservoir in combination with a fractional-flow model. Genetic algorithm is used to solve the developed CRM. Results of this developed model show an appropriate agreement with those of a grid based commercial simulator. In comparison with commercial simulator results, the average relative error of 4.13% is obtained for estimation of oil production by the developed model. Also, the new approach is applied for a real layered reservoir and it is shown how the improvements in the model can improve the results.