Detection of Reservoir Fractures based on Directional Texture Analysis and Self-Organizing Map

Authors

Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Abstract

Due to the significant impact of fractures on the better and more effective production and harvesting, the identification of this phenomenon is a very important. Imaging logs are very powerful tools to study the fractures in the boreholes. In an imaging log, a fracture is seen in the form of a sine curve. In this paper, first, the useful features are extracted by using feature extraction methods such as the Gabor filter bank, Zernike moments, Hu's seven invariant moments and directional Walsh-Hadamard transform, and then, a SOM neural network is used to classify the imaging log. The experimental results showed that the proposed algorithm is able to detect the existing fractures successfully with high accuracy in the imaging logs. In the proposed algorithm, the feature extraction methods are used, which are suitable for extracting the feature of texture objects. The results show that the accuracy of the proposed method is very high to extract fracture pixels, and it has also low sensitivity to noise in the imaging logs. The proposed algorithm in this paper was applied to two types of FMI and RMI image datasets, and the result of the classification has better accuracy in comparison with other algorithms.

Keywords


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