Model-based optimization of waterflooding process has found significant scope for improvement of the economic life-cycle performance of oil fields due to geological and economic uncertainties compared to the conventional reactive strategies. A new frequency-based system identification method is proposed in this paper to identify a robust Multi-Input, Multi-Output (MIMO) surrogate model for an oil reservoir under waterflooding process to describe all the injector-producer relationships. In contrast to the conventional modeling methods, the proposed data-driven modeling approach uses the available injection and production rates as reservoir input-output data. Meanwhile, it includes a structured-bounded uncertainty model in the form of norm-bounded state-space function blocks to account for uncertainties. This facilitates the identified model to be employed in robust control methodology using linear matrix inequality (LMI) problem formulation to eliminate the effect of model uncertainty. The identified MIMO surrogate model is integrated with a desired nonlinear net present value (NPV) objective function in a Multi-Input, Single-Output (MISO) system configuration to synthesize a model-based optimization prediction for economic operation and production of oil from oil reservoirs under both geological and economic uncertainties. The introduced approach is implemented on “EGG model” as a well-recognized three-dimensional synthetic oil reservoir with 8 water injection wells and 4 oil production wells. The results clearly demonstrate that economic performance prediction of the oil reservoir, having uncertain permeability field, lies in the evaluated bound of the uncertainty model.