Abstract

The accuracy and robustness of numerical models of geologic CO2$$\hbox {CO}_2$$ sequestration are almost never quantified with respect to direct observations that provide a ground truth. Here, we conduct CO2$$\hbox {CO}_2$$ injection experiments in meter-scale, quasi-2D tanks with porous media representing stratigraphic sections of the subsurface, and compare them to 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), with respect to simulation accuracy; and (2) the forecasting capability of history-matched numerical models, when applied to different settings. We match three versions of a numerical simulation model—each with access to an increasing level of local data—to a CO2$$\hbox {CO}_2$$ injection experiment in Tank 1 (89.7×47×1.05$$89.7\times 47 \times 1.05$$ cm). Matching is based on a quantitative comparison of CO2$$\hbox {CO}_2$$ migration at different times from timelapse image analysis. Next, use the matched models to make a forecast of a different injection scenario in Tank 1 and, finally, a different injection scenario in Tank 2 (2.86×1.3×0.019$$2.86\times 1.3\times 0.019$$ m), which represents an altogether different stratigraphic section. The simulation model can qualitatively match the observed free-phase and dissolved CO2$$\hbox {CO}_2$$ plume migration and convective mixing. Quantitatively, simulations are accurate during the injection phase, but their concordance decreases with time. Using local data reduces the time required to history match, although the forecasting capability of matched models is similar. The sand–water–CO2(g)$$\hbox {CO}_{2(\text {g})}$$ system is very sensitive to effective permeability and capillary pressure changes; where heterogeneous structures are present, accurate deterministic estimates of CO2$$\hbox {CO}_2$$ migration are difficult to obtain.