The analysis of well logs is important on reservoir characterization. One goal of this analysis is to classify lithofacies in order to estimate the reserve of petroleum. This analysis is traditionally conducted as a semi-automated process, where graphs of curves are used to allow human experts to perform the classification task. On the other hand, this problem has been dealt with in the literature as an automatic pattern recognition task. In this context, several aspects have been investigated such as, comparison among classification methods, reservoirs with heterogeneity and data from different wells. This paper addresses the aspect of using data obtained from one well to train an ensemble of heterogeneous classifiers in order to combine their decision to assign labels to data extracted from other wells. All investigated wells are from the same reservoir. In addition, we show that the classifier ensemble does not outperform its members when the training and the test sets are composed of samples obtained from the same well. This comparison indicates that once a human expert has manually classified lithofacies from one well, this information may be used to train a classifier ensemble, which will be able to use this knowledge to achieve high accuracy on classifying samples from wells within the neighboring region, at the same reservoir.
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