Olivia Dias, L., Bom, C. R., Faria, E.L., Valentín, M.B., Correia, M.D., Albuquerque, M.P., de Albuquerque, M.P. and Coelho, J.M. (2020) Automatic Detection of Fractures and Breakouts Patterns in Acoustic Borehole Image Logs Using Fast-Region Convolutional Neural Networks. Journal of Petroleum Science and Engineering, 191, 107099.
https://doi.org/10.1016/j.petrol.2020.107099

has been cited by the following article:

• TITLE: Applied Machine Learning Methods for Detecting Fractured Zones by Using Petrophysical Logs

  AUTHORS: Haleh Azizi, Hassan Reza

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

  JOURNAL NAME: Intelligent Control and Automation, Vol.12 No.2, May 17, 2021

  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%.