  The accuracy and robustness of numerical models of geologic CO$_2$
sequestration are almost never quantified with respect to direct observations
that provide a ground truth. Here, we conduct CO$_2$ injection experiments in
meter-scale, quasi-2D tanks with porous media representing stratigraphic
sections of the subsurface, and combine them with numerical simulations of
those experiments. We evaluate (1) the value of prior knowledge of the system,
expressed in terms of ex-situ measurements of the tank sands' multiphase flow
properties (local data), to obtain an accurate simulation; and (2) the
predictive capability of the matched numerical models, when applied to
different settings. We match three different simulation models-each with access
to an increasing level of local data-to a CO$_2$ injection experiment in tank 1
(89.7$\times$47$\times$1.05 cm). Matching is based on a quantitative comparison
of CO$_2$ migration at different times from timelapse image analysis. Next, we
simulate a different injection scenario in tank 1, and, finally, a different
injection scenario in tank 2 (2.86$\times$1.3$\times$0.019 m), which represents
an altogether different stratigraphic section. Our models can qualitatively
match the CO$_2$ plume migration and convective mixing of the experimental
truth. Quantitatively, simulations are accurate during the injection phase but
their performance decreases with time. Using local data reduces the time
required to history match. The predictive capability of matched models,
however, is found to be similar. The sand-water-CO$_{2(\mathrm{g})}$ system is
very sensitive to effective permeability and capillary pressure changes; where
heterogeneous structures are present, accurate deterministic estimates of
CO$_2$ migration are unlikely.
