Each of the two major mineral components found in shale samples-organic matter (OM) and inorganic matter (iOM)-has a distinct pore system revealed by scanning electron microscope images, low-pressure nitrogen adsorption, and high-pressure mercury injection tests. Although a vast amount of research has been conducted to detect and measure pore sizes in OM and iOM separately, the connectivity of the pores in these two components remains unclear. In permeability models, pore connectivity between OM and iOM components plays an important role in studying and predicting fluid flow. We studied pore connectivity between OM and iOM by developing pore-network models to mimic the composite nature of distributed OM patches in shale. Input parameters to generate network models were porosity, pore- and throat-size distribution, and total organic content. Mercury injection and capillary-pressure curves were then simulated through generated network models using percolation theory. To study the effects of pore connectivity between OM and iOM, we changed the size and locale of OM patches in the generated network models. Simulation results showed that the locale of OM affects mercury saturation (location and numbers of invaded pores) at given applied pressures. To study the effect of pore-size overlap between OM and iOM pores, we simulated mercury injection for two groups of constructed pore networks: non-overlapping and overlapping. In non-overlapping cases, first all iOM pores were invaded with mercury; then, only OM pores at very high pressure were invaded. In overlapping cases, OM and iOM pores can be invaded simultaneously because some of the pores have similar sizes in both components. The simulated capillary-pressure curves show distinct behavior in the non-overlapping and overlapping cases. Non-overlapping capillary-pressure curves show a sudden increase when OM pores are invaded, whereas overlapping capillary-pressure curves are smoother. Results of this work increase understanding of the connectivity of pores from measured capillary-pressure curves for further implementation in permeability-predictive models.