The Arbuckle Group in the Wellington field in Kansas has recently been a focal point for carbon dioxide (CO₂) geosequestration feasibility studies, mainly through petrophysical, geophysical, and geostatistical modeling methods. Limited well control and the geologic complexity of the study area add uncertainty to mapping the variability in subsurface properties. Therefore, the spatial variability of petrofacies and corresponding petrophysical properties of the Arbuckle Group are further delineated by integrating seismic-scale three-dimensional (3-D) petrofacies and petrophysical-property trends with well log and core data. This approach enables the creation of 3-D seismic-constrained models for petrofacies, porosity, and permeability of the Arbuckle Group through geostatistical modeling. The seismic-constrained models reveal the lateral and stratigraphic heterogeneity of petrofacies, porosity, and permeability. Low permeability petrofacies of the middle Arbuckle interval act as baffles and barriers to fluid flow. Relatively higher porosity and permeability petrofacies in both the lower and upper Arbuckle are candidate injection zones. The theoretical CO₂ storage resources of the study area were calculated using the equation for saline aquifers. The storage resources were estimated to be 0.95, 5.41, and 22.5 Mt for low-, median-, and high-case scenarios, respectively. Using these static models, CO₂ injection simulation was performed to evaluate the subsurface behavior of a theoretical CO₂ plume for long-term carbon storage potential. Dynamic simulation results show that the CO₂ plume disperses laterally and is contained within the injection zone during both injection and postinjection periods. The workflow followed in this study allowed the integration of supervised machine learning and seismic information to further constrain the geostatistical models. Compared to previous studies, this workflow created 3-D reservoir models that show stratigraphic variability of subsurface properties in greater detail and further reduce the subsurface uncertainty in the study area.