Seismic attributes play an important role during reservoir characterization and three-dimensional (3D) lithofacies modeling by providing indirect insight of the subsurface. Using seismic attributes for such studies has always been challenging because it is difficult to determine a realistic relationship between hard data points (i.e., well information) and a 3D volume of seismic attributes. However, a probability-based approach for 3D seismic attribute calibration with well data provides better results of lithofacies modeling and spatial distribution of reservoir properties. This paper presents a probability-based seismic attribute calibration technique that has been described for 3D lithofacies modeling and distribution. This approach helps in subsurface reservoir characterization and provides a realistic lithofacies distribution model. This approach also helps reduce uncertainty of lithofacies prediction compared to conventional methods of simply using geostatistical algorithms.