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ABSTRACT: A new approach to predict log properties from seismic data using multiple seismic attributes and neural networks

Schuelke, James S.1, Dan Hampson2, John Quirein3 (1) Mobil Technology Company, Dallas, TX (2) Hampson-Russell Software Services Ltd, N/A (3) Mobil Technology Company, N/A

In this paper, we describe a new method for predicting well log properties from seismic data. The analysis data consists of a series of target logs from wells which tie a 3-D seismic volume. From the 3-D seismic volume a series of sample-based attributes is calculated. The objective is to derive a multi-attribute transform, which is a linear or non-linear transform between a subset of the attributes and the target log values. In the linear mode, the transform consists of a series of weights, which are derived by least-squares minimization. In the non-linear mode, a neural network is trained, using the selected attributes as inputs.

To estimate the reliability of the derived multi-attribute transform, cross-validation is used. In this process, each well is systematically removed from the training set, and the transform is re-derived from the remaining wells. The prediction error for the hidden well is then calculated. The validation error, which is the average error for all hidden wells, is used as a measure of the likely prediction error when the transform is applied to the seismic volume.

The method is applied to two real data sets. In each case, we see a continuous improvement in predictive power as we progress from single-attribute regression to linear multi-attribute prediction to neural network prediction.

AAPG Search and Discovery Article #90913©2000 AAPG International Conference and Exhibition, Bali, Indonesia