Abstract
We discuss the use of the Gamma test as a powerful tool for selecting the best combination of seismic attributes to predict porosity (φe). The data consists of four wells with conventional logs which tie to a 3-D seismic volume. A formation evaluation was done. Both well-logs and seismic data are used to train an artificial neural network to get estimates of effective porosity, φe. The validation based on leave-one-out was poor. We then generated a complementary set of synthetic data (from the original well-log data), varying the effective porosity and applying the Gassmann’s equation for fluid substitution. The synthetic well-logs we obtained were used to train the neural networks giving better validation and more accurate results.
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