In this paper, we combine the methods of geostatistics and multiattribute prediction for the integration of seismic and well-log data, and illustrate this new procedure with a case study.
Our case study involves the prediction of porosity in the Blackfoot field of central Alberta. The objectives of the survey were to delineate the channel and distinguish between sand-fill and shale-fill. The input consisted of twelve porosity logs as well as a 3-D seismic volume, and the inversion of this volume. We found an excellent correlation between porosity and the inverted acoustic impedance volume. However, we found that the newly proposed method created an improved final result.
Our approach uses the well logs in the area to "train" the neural network. We first extract average porosity values at the zone of interest, and then compare these values to average seismic attributes over the same zone. The technique of cross-validation is then 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. Our results are very encouraging, and delineate the channel sand much more clearly than on the original seismic volume, as well as giving a better fit to the observed well logs.
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