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Applied Machine Learning Methods for Detecting Fractured Zones by Using Petrophysical Logs
School of Electrical Engineering and Computer Sciences, University of North Dakota, Grand Forks, ND, USA
School of Electrical Engineering and Computer Sciences, University of North Dakota, Grand Forks, ND, USA
- 1 School of Electrical Engineering and Computer Sciences, University of North Dakota, Grand Forks, ND, USA
- 2 School of Electrical Engineering and Computer Sciences, University of North Dakota, Grand Forks, ND, USA
Intelligent Control and Automation·Volume 12 (2021)·Pages 44–64·Published 17 May 2021·DOI10.4236/ica.2021.122003
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Abstract
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%.
KeywordsDecision TreeDeep LearningOrdered Weighted AveragingRandom For-estSupport Vector Machine
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