Robust reservoir rock fracture recognition based on a new sparse feature learning and data training method

  • Fatemeh Taibi
  • Gholamreza AkbarizadehEmail author
  • Ebrahim Farshidi


In this paper, the main goal is to identify the sine fractures of reservoir rock automatically. Therefore, a five-step algorithm is applied on the imaging logs. The first step consists of extracting the features of the imaging log by applying the Zernike moments. In the second step, the features are learned by using sparse coding. In the third step, the imaging log is segmented by using the self-organizing map neural network and the training dataset. In the fourth step, the fracture points are extracted by Steger method. In the last step, to determine the sine parameters of fractures, the Hough transform is applied to the image fracture points. The experimental results show that the proposed algorithm is highly able to detect the fractures of the imaging logs successfully. Also, the precision of the proposed method to extract the fracture pixels is so high and it has low sensitivity to noise in the imaging logs. In this paper, the proposed algorithm has been applied on the imaging datasets of FMI and the obtained results show that the classification has better precision compared with other proposed algorithm.


Imaging log Zernike moments Sparse coding Self-organizing neural network Steger method Hough transform 



The work described in this paper was supported by the Shahid Chamran University of Ahvaz, as an M.Sc. thesis under Grant No. 97/3/02/26247. The authors would like to thank the Shahid Chamran University of Ahvaz for financial support. Also, the authors would like to thank the National Iranian Drilling Company (NIDC) for providing the image dataset.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Faculty of EngineeringShahid Chamran University of AhvazAhvazIran

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