In this paper, we combine the methods of geostatistics and multi-attribute reservoir parameter prediction (the multi-attribute transform) for the integration of seismic and well log data, and illustrate this new procedure with a case study involving the prediction of porosity at the Blackfoot oilfield, central Alberta. The objectives of the survey were to delineate incised, valley-fill sediments within the Early Cretaceous Glauconitic Formation at this field and to distinguish between sand-fill and shale-fill. The input consisted of twelve porosity logs together with a 3D seismic volume and the inversion of this volume. Although an excellent correlation was found between porosity and the initial inverted acoustic impedance volume, the combination of traditional geostatistics and the multi-attribute transform produced an improved final result.

    Our approach uses well logs to "train" the multi-attribute transform algorithm. We first extract average porosity values over the depth zone of interest, and compare these values to average seismic attributes over the same zone. Cross-validation is used to show which attributes are significant. We then apply the results of the training and cross-validation to data slices derived from both the seismic data cube and the inverted cube to produce an initial porosity map. Finally, we improve the fit between the well-log values and the porosity map using cokriging.