Numerical simulations use past reservoir behavior to calibrate models used to predict future performance. Traditionally, this process is carried out deterministically through history matching and most current approaches focus on developing probabilistic procedures, called data assimilation, whereby reservoir simulation models are calibrated to reproduce plausible performance under different operating conditions. The output of different data-assimilation strategies can over-reduce the variability by having several highly-similar scenarios. Consequently, the need to ensure the variability of simulation models arises, to consider multiple possible solutions. In this vein, we introduce a visual analytics approach, based on phylogenetic trees, as a means to evaluate the variability of numerical reservoir simulation models throughout the probabilistic data assimilation process. Phylogenetic trees arrange simulation results based on similarity and visually convey match quality through color encoding. We applied our methodology to two scenarios: (i) a synthetic scenario to exemplify the properties of the phylogenetic tree for the analysis of simulation models; and (ii) two different ensembles of simulation models, each representing a data-assimilation iteration, from the UNISIM-I-H benchmark case based on the Namorado Field, Campos Basin, Brazil. Our strategy is intuitive and easy-to-use, allowing the user to assess the similarity of the numerical reservoir scenarios, ensemble variability, and match improvement during data assimilation iterations.