Geostatistics – Kriging and Co-Kriging Methods in Reservoir Characterization of Hydrocarbon Rock Deposits

Abstract

Abstract Understanding the spatial distribution of the physical and chemical properties of rocks and the fluids they contain is very essential in determining hydrocarbon deposits, source, seals and aquifers. Population of petrophysical data for reservoir analysis has always been challenging in hydrocarbon exploration with little or no knowledge about the reservoir or with few pilot wells. Several basic interpolation methods, and geostatistical interpolation methods such as kriging and co-kriging have been used to populate the petrophysical parameters for better analysis but without much consideration for removal of inherent data error. The need then arises for the data to be populated to be error free because an abnormal or anomalous data set would not be representative, irrespective of the accuracy of the model. In this study, the Gamma test (GT) which is a non-parametric technique is used to assess the quality of the data since it is independent on the geostatistical model. Five workflow that took cognizance of GT filtered and unfiltered data assemblage were used. The data is then populated in the models by using geostatistical methods of kriging and co-kriging to interpolate the petrophysical data for further analysis thereby reducing the uncertainties faced during reservoir characterization and reserves estimation of in-place hydrocarbon and understanding of intrinsic reservoir heterogeneities. Results were compared to the results obtained when using both a basic interpolation method: inverse distance method, and geostatistical methods without proper data assessment. Compared to the other interpolation methods, this technique achieved a lower estimation variance error hence can be said to create a better representation of the reservoir petrophysical parameters.