There are wide and crucial applications of the reservoir simulation to develop, manage and monitor reservoirs. However, the traditional simulation methods have different limitations such as noticeable necessary time to prepare and model data and uncertainties in the available data. Therefore, new approaches are developed to offer rapid reservoir simulation with lower and more reliable data to manage and optimize production from hydrocarbon reservoirs and provide us a quick estimation of the performance of the reservoirs. One of these approaches is the Capacitance-Resistive Model (CRM). CRM is a quantitative technique based on material balance that uses only injection/production rates and well coordinates to identify and quantify reservoir model parameters, which are well connectivity and time constant values. In CRM, the reservoir is assumed as a system where the injection rates are the input signals and the production rates are the output signals. The injection performance over a period of time can be identified by estimating fractions of injected fluid to each producer and the time taken for a producer to sense the injections. With CRM, it is possible to optimize a waterflooding operation in a field by reallocating the water injection rates to optimize the oil production. This tool is preferred due to its simplicity, the short computation time and the use of available field data. In this study, we developed a new approach to use the Capacitance Resistive method to optimize the waterflooding process in an Omani oil field by a ranking approach for the injectors. By reallocation of the available water to injectors, the future oil production was optimized for the next three years. This method showed that the optimized scenario leads to 29.90% increase in future oil production compared to the current injection rates profile. This was achieved by injecting 59% of available water for most effective wells and the remaining amount for other wells.