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

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