  Carbon dioxide (CO2) trapping in capillary networks of reservoir rocks is a
pathway to long-term geological storage. At pore scale, the CO2 trapping
potential depends on injection pressure, temperature, and the rock's
interaction with the surrounding fluids. Modeling this interaction requires
adequate representations of both capillary volume and surface. For the lack of
scalable representations, however, the prediction of a rock's CO2 storage
potential has been challenging. Here, we report how to represent a rock's pore
space by statistically sampled capillary networks (ssCN) that preserve
morphological rock characteristics. We have used the ssCN method to simulate
CO2 drainage within a representative sandstone sample at reservoir pressures
and temperatures, exploring intermediate- and CO2-wet conditions. This wetting
regime is often neglected, despite evidence of plausibility. By raising
pressure and temperature we observe increasing CO2 penetration within the
capillary network. For contact angles approaching 90 degrees, the CO2
saturation exhibits a pronounced maximum reaching 80 percent of the accessible
pore volume. This is about twice as high as the saturation values reported
previously. For enabling validation of our results and a broader application of
our methodology, we have made available the rock tomography data, the digital
rock computational workflows, and the ssCN models used in this study.
