This work presents a qualitative analysis of the uncertainties in performance prediction for a reservoir characterization process in which static and dynamic data are combined. Using primary, single-phase production data as the dynamic constraint in the inversion, we evaluate the quality of the models when they are used in alternative or subsequent production scenarios. We conclude that, provided the well configuration remains the same as that used for the dynamic constraint in the inversion, the models perform well when the production stage remains single-phase, primary depletion, even at the individual well level and for future prediction. For multiphase primary production, there is also a reasonable match in performance, although not as good as for a single-phase case. This is because such depletion processes are controlled by the permeability in the near-wellbore region, which is adequately reconstituted by the characterization process used; we observed, however, that the multiphase density variations with time had little impact. However, the fine scale, inter-well permeability patterns of the different realizations obtained by the integration of the dynamic and static data are not sufficiently resolved and so are unable to capture adequately the inter-well permeability heterogeneity patterns that are necessary for accurate waterflood performance matching. Thus, the models produced by inversion in which primary recovery data are used as a constraint are inadequate in predicting the performance of processes involving injection.