Author(s)

Haleh Azizi^*, Hassan Reza

School of Electrical Engineering and Computer Sciences, University of North Dakota, Grand Forks, ND, USA.

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%.

Decision Tree, Deep Learning, Ordered Weighted Averaging, Random For-est, Support Vector Machine

Azizi, H. and Reza, H. (2021) Applied Machine Learning Methods for Detecting Fractured Zones by Using Petrophysical Logs. Intelligent Control and Automation, 12, 44-64. doi: 10.4236/ica.2021.122003.