Plug and perf hydraulic fracturing is performed with high-pressure injection of fluid and proppant from perforation clusters along a wellbore. During this process, uniform placement of fluid and proppant is important for maximizing economic performance. In prior work, we developed a wellbore-proppant transport simulator, which accounts for a wide range of phenomena, including proppant suspension, proppant settling, perforation erosion, perforation pressure drop, inertial effects, perforation orientation, and random variance, among others. In the present work, we calibrate the simulator to downhole imaging measurements of perforation erosion from wells in the Midland Basin, Montney, and Bakken Shale plays. The simulator uses several empirical coefficients. We identify coefficients that have consistent values in the calibrations to all datasets. On the other hand, a few of the coefficients exhibit variability from dataset to dataset. We show how these parameters can be calibrated on a case-by-case basis prior to using the simulator for design optimization. Based on these case studies, we identified several opportunities to improve the simulator physics – by accounting for perforation ‘inline’ effects, including random variance in erosion coefficient, and increasing the amount of proppant suspension. Comparison across datasets shows that there is not a single consistent trend in heel-side or toe-side erosion bias. Different physical processes have opposing effects on heel/toe-side bias, and depending on the stage design and practical conditions, these processes can have different relative magnitudes. Correspondingly, the optimal perforation design varies from case-to-case, depending on which type of ‘bias’ is observed in the base design. The simulator predicts that measured erosion uniformity should be lower than the proppant or slurry uniformity. This result is supported by observations from a Bakken dataset in this study, where fiber-based slurry allocations yielded a significantly higher uniformity index than downhole imaging-based measurements. The implication is that the ‘uniformity index’ of erosion, observed from downhole imaging, cannot be taken as a direct proxy for the uniformity of proppant or fluid outflow. Finally, the simulator was applied to each field dataset to identify the optimal perforation design. The optimization procedure identified opportunities to improve the cluster-level uniformity index of proppant placement by a range of 0.12 to 0.19. Because each dataset requires case-specific calibration prior to optimization, there is no single ‘best’ design for all circumstances.