Three-dimensional reservoir models are used routinely for various purposes in the E&P business to support value-based decisions. One of the key challenges in 3D reservoir modeling is distributing the identified facies and their associated properties in the defined 3D structural/stratigraphic framework respecting geologic knowledge and available well data. Different geostatistical techniques are used for populating the reservoir facies and properties in the 3D reservoir models which have different working inputs and assumptions. Most reservoir-characterization studies use variogram-based geostatistical-modeling methods to accurately and efficiently represent reservoir heterogeneities. The variogram-based techniques constrain 3D reservoir models to local data which represent the geologic knowledge and help to create appropriate flow behaviors through dynamic simulation. However, simulation results obtained from those techniques are highly dependent on available data, selected variogram model, and the geomodeler's geostatistical knowledge and geologic experience. To illustrate the impact of variogram modeling on 3D reservoir-modeling outcomes, multiple 3D reservoir models were generated using different variograms for a siliciclastic reservoir. As expected, the results obtained from the models show significant variations, which indicate that selection of appropriate variogram models, which is critical for facies and property distribution in 3D static models, affects original hydrocarbon in place (OHIP) and recoverable resources/reserves estimation and production forecasts. A sensitivity analysis of the variogram parameters in the 3D static models and its impact in the dynamic simulation should be considered an integral part of the 3D modeling workflow.