1. A computer-implemented method for model selection, the computer-implemented method comprising: (a) providing an ensemble of reservoir models for a reservoir that define an input uncertainty space; (b) inputting a target number of representative models and one or more target percentiles of output variables that the representative models are to approximate; (c) selecting the representative models from the ensemble of reservoir models that match the one or more target percentiles of output variables while maximizing the spread between the selected representative models in the input uncertainty space, wherein selecting the representative models from the ensemble of reservoir models comprises solving a single constrained minimax optimization problem, and wherein the single constrained minimax optimization problem is represented mathematically as: for i = 1: nTrials for j = 1: P X j = arg ⁢ ⁢ rand X j ∈ C ⁢ { ( max k ⁢ ⁢ min l ⁢  y k j - y _ k l y _ k l  ) < ɛ } ∀ k = 1, . . . , M: l = 1, . . . , P − j + 1 sub to: ∀ q = j − 1, . . . , 1 nnz(prctile(Y j ) == prctile(Y q )) = 0 end end; and (d) modeling a behavior of the reservoir using the selected representative models.
3. The computer-implemented method of claim 1 , wherein the single constrained minimax optimization problem is solved by Markov chain Monte Carlo methods.