1. A system for predicting fluid flow in a subterranean reservoir having non-Gaussian characteristics, the system comprising: a computer processor configured to execute computer readable program code for accessing an ensemble of initial models representing a subterranean reservoir having non-Gaussian characteristics; a data source accessible to the computer processor for collecting reservoir field data for a predetermined duration of time; model update program code, executable by the computer processor, for receiving the reservoir field data, and for using the field data with a kernel-based ensemble Kalman filter to update the initial models of the ensemble at a predetermined time such that data from the updated initial models are consistent with the field data and such that the non-Gaussian characteristics of the reservoir in the updated initial models are preserved, thereby maximizing accuracy of reservoir prediction data to be generated by the updated initial models, wherein using the kernel-based ensemble Kalman filter includes using an equation, wherein the equation is: y n + 1 = θ 1 + θ ⁢ y n + 1 1 + θ ⁢ ∑ i = 1 M ⁢ ⁢ b ij ⁢ ∑ k = 1 q ⁢ ⁢ k ⁡ ( y i f · y n ) k - 1 ⁢ y i f ∑ k = 1 q ⁢ ⁢ k ⁡ ( y n · y n ) k - 1 ∀ j = 1 , … ⁢ , M wherein θ is a relaxation factor; wherein M refers to number of ensemble members of the ensemble of initial models; wherein y is a state vector; wherein n is an iteration number; wherein y f is a forecasted state vector; wherein q refers to an order of the kernel; and wherein b is a coefficient of the kernel-based ensemble Kalman filter.