Advertisement

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

  • Fatemeh Taibi
  • Gholamreza AkbarizadehEmail author
  • Ebrahim Farshidi
Article
  • 15 Downloads

Abstract

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.

Keywords

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

Notes

Acknowledgements

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.

References

  1. Abolghasemi, V., Ferdowsi, S., & Sanei, S. (2012). Blind separation of image sources via adaptive dictionary learning. IEEE Transactions on Image Processing, 21(6), 2921–2930.MathSciNetzbMATHGoogle Scholar
  2. Aharon, M., Elad, M., & Bruckstein, A. (2006). K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11), 4311–4322.zbMATHGoogle Scholar
  3. Al-Sit, W., Al-Nuaimy, W., Mar, M., & Al-Ataby, A. (2015). Visual texture for automated characterisation of geological features in borehole televiewer imagery. Journal of Applied Geophysics, 119, 139–146.Google Scholar
  4. Al-Sit, W. (June 2015). Automatic feature detection and interpretation in borehole data. Thesis of the University of Liverpool for the degree of Doctor in Philosophy.Google Scholar
  5. Ansari, M. A., & Dixit, M. (2017). An image retrieval framework: A review. International Journal of Advanced Research in Computer Science, 8(5), 692–699.Google Scholar
  6. Assous, S., Elkington, P., Clark, S., & Whetton, J. (2014). Automated detection of planar geologic features in borehole images. Geophysics, 79(1), 11–19.Google Scholar
  7. Cha, M., Phillips, R., & Yee, M. (2011). Finding curves in SAR CCD Images. In IEEE international conference on acoustics, speech and signal processing (ICASSP). Google Scholar
  8. Changchun, Z., & Ge, S. (2002). A Hough transform-based method for fast detection of fixed period sinusoidal curves in images. In 6th international conference on signal processing. IEEE.Google Scholar
  9. Chen-Yin, X., Hao, H. W., Wang, Z. B., Huang, K., & Lui, Q. (2011). FMI image based rock structure classification using classifier combination. Neural Computing and Applications, 20(7), 955–963.Google Scholar
  10. Cyganek, B. (2012). One-class support vector ensembles for image segmentation and classification. Journal of Mathematical Imaging and Vision, 42(2), 103–117.MathSciNetzbMATHGoogle Scholar
  11. Elazab, A., Wang, C., Jia, F., Wu, J., Li, G., & Hu, Q. (2015). Segmentation of brain tissues from magnetic resonance images using adaptively regularized kernel-based fuzzy C-Means clustering. Computational and Mathematical Methods in Medicine, 2015, 1–12.zbMATHGoogle Scholar
  12. Ergin, S., & Kilinc, O. (2014). A new feature extraction framework based on wavelets for breast cancer diagnosis. Computers in Biology and Medicine, 51, 171–182.Google Scholar
  13. Fernández, S. A., Curiale, A. H., & Ferrero, G. V. S. (2015). A local fuzzy thresholding methodology for multiregion image segmentation. Knowledge-Based Systems, 83, 1–12.Google Scholar
  14. Fisk, D. L. (1965). Quasi-martingales. Transactions of the American Mathematical Society, 120(3), 359–388.MathSciNetzbMATHGoogle Scholar
  15. Ghamisi, P., Couceiro, M. S., Benediktsson, J. A., & Ferreira, N. M. F. (2012). An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Systems with Applications, 39(16), 12407–12417.Google Scholar
  16. Hu, M. K. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8, 179–187.zbMATHGoogle Scholar
  17. Jiang, G., Wang, X., Wang, Z., & Liu, H. (2016). Wheat rows detection at the early growth stage based on Hough transform and vanishing point. Computers and Electronics in Agriculture, 123, 211–223.Google Scholar
  18. Kingdon, A., Fellgett, M. W., & Williams, J. D. O. (2016). Use of borehole imaging to improve understanding of the in situ stress orientation of Central and Northern England and its implications for unconventional hydrocarbon resources. Marine and Petroleum Geology, 73, 1–20.Google Scholar
  19. Kohonen, T. (2013). Essentials of the self-organizing map. Neural Networks, 37, 52–65.Google Scholar
  20. Li, C., Gore, J. C., & Davatzikos, C. (2014). Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magnetic Resonance Imaging, 32, 913–923.Google Scholar
  21. Li, W., Liu, J., & Du, Q. (2016). Sparse and low-rank graph for discriminant analysis of hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 54(7), 4094–4105.Google Scholar
  22. Li, Z., Mao, Y., Huang, W., Li, H., Zhu, J., Li, W., et al. (2017). Texture-based classification of different single liver lesion based on SPAIR T2 W MRI images. Bio Med Central Medical Imaging, 17(42), 1–9.Google Scholar
  23. Liang, M., Peng, S., Du, W., & Lu, Y. (2018). Tectonic stress estimation from ultrasonic borehole image logs in a coal bed methane well, northeastern Qinshui Basin, China. Journal of Natural Gas Science and Engineering, 52, 44–58.Google Scholar
  24. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.Google Scholar
  25. Liu, H., Guo, D., & Sun, F. (2016). Object recognition using tactile measurements: Kernel sparse coding methods. IEEE Transactions on Instrumentation and Measurement, 65(3), 656–665.Google Scholar
  26. Liu, X., Liu, F., Chen, J., Zhao, Z., Wang, A., & Lu, Z. (2018). Resistivity logging through casing response of inclined fractured formation. IEEE Transactions on Geoscience and Remote Sensing, 56(8), 4919–4929.Google Scholar
  27. Liu, C., Sun, Y., & Christopher, L. (2015). 3D EM/MPM image segmentation using an FPGA embedded design implementation. Journal of Signal Processing Systems, 81(3), 411–424.Google Scholar
  28. Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2010). Online learning for matrix factorization and sparse coding. Journal of Machine Learning Research, 11, 19–60.MathSciNetzbMATHGoogle Scholar
  29. Nahm, A. L., & Kattenhorn, S. A. (2015). A unified nomenclature for tectonic structures on the surface of Enceladus. Icarus, 258, 67–81.Google Scholar
  30. Nanda, T., Sahoo, B., & Chatterjee, C. (2017). Enhancing the applicability of Kohonen Self-Organizing Map (KSOM) estimator for gap-filling in hydrometeorological timeseries data. Journal of Hydrology, 549, 133–147.Google Scholar
  31. Otten, K. A., Brischke, C., & Meyer, C. (2017). Material moisture content of wood and cement mortars—Electrical resistance-based measurements in the high ohmic range. Construction and Building Materials, 153, 640–646.Google Scholar
  32. Patel, R., Baghel, S., Patel, S., & Mishra, R. (2018). Live image colour segmentation using different methods of ANN. International Journal on Recent and Innovation Trends in Computing and Communication, 6(1), 21–26.Google Scholar
  33. Rajchl, M., Baxter, J. S. H., McLeod, A. J., Yuan, J., Qiu, W., Peters, T. M., et al. (2016). Hierarchical max-flow segmentation framework for multi-atlas segmentation with Kohonen self-organizing map based Gaussian mixture modeling. Medical Image Analysis, 27, 45–56.Google Scholar
  34. Saedi, G., Soleimani, B., & Esmaeilzadeh, S. (2017). Fracture characterization utilizing FMI, velocity deviation logs, core description and thin sections data. Neues Jahrbuch für Geologie und Paläontologie - Abhandlungen, 284(1), 15–28.Google Scholar
  35. Seifallahi, M., Tokhmechi, B., Soleimani, A., & AhmadiFard, A. (2013). Intelligent identification of open natural fractures using from borehole image logs. In Thirty-second meeting of the first international congress of earth sciences (in Persian) Google Scholar
  36. Shubhi, S., & Pritee, K. (2015). Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM. Journal of Digital Imaging, 28(1), 77–90.Google Scholar
  37. Sun, X., Qu, Q., Nasrabadi, N. M., & Tran, T. D. (2014). Structured priors for sparse-representation-based hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 11(7), 1235–1239.Google Scholar
  38. Tang, Y., Chen, Y., Xu, N., Jiang, A., & Zhou, L. (2016). Image denoising via sparse coding using eigenvectors of graph Laplacian. Digital Signal Processing, 50, 114–122.Google Scholar
  39. Tao, S., Boley, D., & Zhang, S. (2016). Local linear convergence of ISTA and FISTA on the LASSO problem. SIAM Journal on Optimization, 26(1), 313–336.MathSciNetzbMATHGoogle Scholar
  40. Teague, M. (1980). Image analysis via the general theory of moments. Journal of the Optical Society of America, 70(8), 920–930.MathSciNetGoogle Scholar
  41. Teniou, S., & Meribout, M. (2015). A multimodal image reconstruction method using ultrasonic waves and electrical resistance tomography. IEEE Transactions on Image Processing, 24(11), 3512–3521.MathSciNetzbMATHGoogle Scholar
  42. Thapa, B. B., Hughett, P., & Karasaki, K. (1997). Semi-automatic analysis of rock fracture orientations from borehole. Wall Images, Geophysics, 62(1), 129–137.Google Scholar
  43. Thiagarajan, J. J., Ramamurthy, K. N., & Spanias, A. (2014). Multiple kernel sparse representations for supervised and unsupervised learning. IEEE Transactions on Image Processing, 23(7), 2905–2915.MathSciNetzbMATHGoogle Scholar
  44. Torres, D., Strickland, R. W., & Gianzero, M. V. (1990). A new approach to determining dip and strike using borehole images. In Society of Petrophysicists and Well-Log Analysts 31th annual logging symposium (pp. 1–20), Lafayette, Louisiana.Google Scholar
  45. Voorn, M., Exner, U., & Multiscale, A. R. (2013). Hessian fracture filtering for the enhancement and segmentation of narrow fractures in 3D image data. Computers & Geosciences, 57, 44–53.Google Scholar
  46. Wang, W. (2005). An edge based segmentation algorithm for rock fracture tracing. In Proceedings of the computer graphics, imaging and vision: New trends (CGIV’05) (pp. 43–48).Google Scholar
  47. Wang, W., & Liang, Y. (2015). Rock fracture centerline extraction based on Hessian matrix and Steger algorithm. Internet and Information Systems, 9(12), 5073–5086.Google Scholar
  48. Wu, H., Yang, K., & Zeng, Y. (2018). Sparse coding and compressive sensing for overlapping neural spike sorting. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(8), 1516–1525.Google Scholar
  49. Yang, S., Hou, C., Nie, F., & Wu, Y. (2012). Unsupervised maximum margin feature selection via L2, 1-norm minimization. Neural Computing and Applications, 21(7), 1791–1799.Google Scholar
  50. Yang, G., Lalande, V., Chen, L., Azzabou, N., Larcher, T., Certaines, J. D., et al. (2015). MRI texture analysis of GRMD dogs using orthogonal moments: A preliminary study. IRBM, 36(4), 213–219.Google Scholar
  51. Yuan, X. C., Pun, C. M., & Chen, C. L. P. (2013). Geometric invariant watermarking by local Zernike moments of binary image patches. Signal Processing, 93(7), 2087–2095.Google Scholar
  52. Zhang, D., Han, J., Li, C., Wang, J., & Li, X. (2016). Detection of co-salient objects by looking deep and wide. International Journal of Computer Vision, 120(2), 215–232.MathSciNetGoogle Scholar
  53. Zhang, C., Liu, J., Liang, C., Xue, Z., Pang, J., & Huang, Q. (2014a). Image classification by non-negative sparse coding, correlation constrained low-rank and sparse. Computer Vision and Image Understanding, 123, 14–22.Google Scholar
  54. Zhang, X., Zhang, W., & Xiao, X. (2014b). Rapid detection of bedding boundaries based on borehole images. Computer Modelling & New Technologies, 18(10), 207–211.Google Scholar
  55. Zhao, Y., Wang, S., Zhang, X., & Yao, H. (2013). Robust hashing for image authentication using Zernike moments and local features. IEEE Transactions on Information Forensics and Security, 8(1), 55–63.Google Scholar
  56. Zhu, X., Li, X., & Zhang, S. (2016). Block-row sparse multiview multilabel learning for image classification. IEEE Transactions on Cybernetics, 46(2), 450–461.Google Scholar

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

Personalised recommendations