Geostatistical simulation techniques, including Sequential Gaussian Simulation (SGS) and Sequential Indicator Simulation (SIS), are widely used for ore grade modeling and for assessing spatial uncertainty. However, determining an appropriate number of realizations to achieve stable and reliable results remains a practical challenge in applying these methods. This study investigates the impact of the number of realizations on ore grade modeling results obtained using SGS and SIS. In this research, simulations were performed with different numbers of realizations, including 2, 5, 10, 20, 30, 40, and 50. E-type estimates were then extracted after back-transformation to the original data space. To evaluate the performance of the two methods, the mean bias, coefficient of variation (CV), and percentile-based indices (P10, P50, and P90) were analyzed as functions of the number of realizations. In addition, the results of Sequential Indicator Simulation were compared with those of Sequential Gaussian Simulation. The results indicate that SGS exhibits negligible and stable mean bias across all numbers of realizations and is therefore globally unbiased with respect to the mean. In contrast, SIS when using E-type averaging, shows a significant systematic bias relative to the original data. Moreover, uncertainty measures derived from SGS, including the coefficient of variation and the percentile range, decrease as the number of realizations increases and stablize beyond approximately 20 realizations. Based on these findings, 20 realizations are recommended as an optimal choice, providing an appropriate balance between the stability of uncertainty measures and computational cost. Finally, the three-dimensional E-type model and grade–tonnage curves corresponding to the selected number of realizations are presented, confirming the effectiveness of the proposed approach.