In the last decade, a few
valuable types of research have been conducted to discriminate fractured zones
from non-fractured ones. In this paper, petrophysical and image logs of eight
wells were utilized to detect fractured zones. Decision tree, random forest,
support vector machine, and deep learning were four classifiers applied over
petrophysical logs and image logs for both training and testing. The output of
classifiers was fused by ordered weighted averaging data fusion to achieve more
reliable, accurate, and general results. Accuracy of close to 99% has been
achieved. This study reports a significant improvement compared to the existing
work that has an accuracy of close to 80%.
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