Co-krigged porosity modeling exhibits better results than conventional regression analysis and multiattribute transform porosity models. 9th Middle East Geosciences Conference, GEO 2010.

Reservoir heterogeneity characterization is always a real challenge for the sub-surface professionals. Although there is no direct way to assess the true heterogeneity, still certain models can imitate the important features of variability. The spatial distribution of reservoir properties can be determined by stepping through a workflow which starts where standard workstation seismic and geologic interpretation end. In order to obtain the most accurate and detailed results, one must design a multidisciplinary workflow that quantitatively integrates all the relevant subsurface data. This study demonstrates the enhanced results of regression analysis and the multi-attribute transforms which are used for porosity prediction in one of the areas in Middle Indus Basin. The co-krigging method used in geostatistics has been applied to derive a combined effect of both the techniques. The dataset used for this study consists of the available well data including VSP and the petrophysical logs, a 3-D seismic volume consisting of both reflectivity and inversion data for attribute extraction. A conventional regression analysis using the single polynomial function incorporating the AI and the well porosities were used to extrapolate the average porosities away from the known control points. We then applied the multi-attribute transform using various seismic attributes and the well data. A cross-validation of porosity with the significant seismic attributes was done through neural networking. The results were then applied to derive initial porosity map. Both the results were integrated using co-krigging approach which involved creation and comparison of different variograms to get the enhanced version of porosity model. The co-krigged porosity maps showed a better delineation of good porosity zones as compared to initial porosity maps.