The quality of any hydrocarbon-bearing reservoir is vital for a successful exploitation work.. The reservoir quality is a function of its petrophysical parameters. Hence the need to model these properties geostatistically in order to determine the quality away from well locations.Composite logs for four wells and 3-D seismic data were used for the analysis. A reservoir named Sand X was mapped and correlated across wells 1 through 4. The four reservoir quality indicators - Effective porosity, permeability, volume of shale and net-to-gross- were estimated and modelled across the field. Sequential Gaussian simulation algorithm was employed to distribute these properties stochastically away from well locations and five realizations were generated. The volume of shale varied from 0.025 (Well 1, second realization) to 0.18(Well 2, first realization). The net-to-gross varied from 0.81 to 0.96 in wells 3 and 4 respectively, for the third realization, while the effective porosity varied from 0.125 to 0.295 for the fifth realization in Wells 3 and 4 respectively. The permeability is above 5000mD at all the existing well locations.These realizations were ranked using Lp norm statistical tool to pick the best for further evaluation. The reservoir quality deduced from the analyzed indicators was favourably high across the reservoir.The application of geostatistics has laterally enhanced the log data resolution away from established well locations.