A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Applied Machine Learning Methods for Detecting Fractured Zones by Using Petrophysical Logs
2021
Intelligent Control and Automation
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
doi:10.4236/ica.2021.122003
fatcat:zlticsw5wbeexa2gvfpy52p77y