1887

Abstract

Summary

The computational power of the reservoir modelling is growing nowadays enabling the use of more precise core descriptions. The industry needs high accuracy models for precise reserves estimation. As a way to improve that, different authors proposed semiautomatic image segmentation algorithms based on color spaces approaches. The segmentation algorithms are common in machine vision as most images consist of semantically different parts. This paper focuses on the review and application of different machine vision algorithms for semi-supervised segmentation of full core images based on superpixel approach. Such an approach takes into account pixel groups with their semantic (texture, intensities, etc.) meaning. The reviewed algorithms can contribute to the precise description of rocks at different scales. The automatic way to segment lithotypes and other characteristics of rock introduced. U-Net like convolutional neural network fine-tuned on a small dataset may produce meaningful results.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201953061
2019-11-05
2025-02-16
Loading full text...

Full text loading...

References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and Süsstrunk, S.
    [2010]. SLIC Superpixels. EPFL Technical Report 149300. Retrieved from http://www.kev-smith.com/papers/SLIC_Superpixels.pdf
    [Google Scholar]
  2. Baraboshkin, E. E., Ismailova, L. S., Orlov, D. M., Zhukovskaya, E. A., Kalmykov, G. A., Khotylev, O. V., Baraboshkin, E. Y., and Koroteev, D. A.
    Deep Convolutions for In-Depth Automated Rock Typing. Computers & Geosciences, [submit].
    [Google Scholar]
  3. Bradski, G.
    [2000]. The OpenCV Library. Dr. Dobb’s Journal of Software Tools.
    [Google Scholar]
  4. Chollet, F.
    , Google, Microsoft, and Others. [2015]. Keras.
    [Google Scholar]
  5. Felzenszwalb, P. F., and Huttenlocher, D. P.
    [2004]. Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 59(2), 167–181. https://doi.org/10.1023/B:VISI.0000022288.19776.77
    [Google Scholar]
  6. Hunter, J. D.
    [2007]. Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55
    [Google Scholar]
  7. Khasanov, I. I.
    (Gubkin R. state university of oil and gas). [2014]. Rock color distribution analysis of core images (in Russian). OIL AND GAS GEOLOGY, (5), 33–39.
    [Google Scholar]
  8. Long, J., Shelhamer, E., and Darrell, T.
    [2014]. Fully Convolutional Networks for Semantic Segmentation. Retrieved from http://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf
    [Google Scholar]
  9. MartinAbadi, AshishAgarwal, PaulBarham, B.E., ZhifengChen, CraigCitro, Greg S.Corrado, A. D., JeffreyDean, MatthieuDevin, SanjayGhemawat, I. G., AndrewHarp, GeoffreyIrving, MichaelIsard, RafalJozefowicz, Y. J., LukaszKaiser, ManjunathKudlur, JoshLevenberg, DanMané, M. S., RajatMonga, SherryMoore, DerekMurray, ChrisOlah, J. S.,... Zheng, X.
    [2015]. TensorFlow: Large-scale machine learning on heterogeneous systems.
    [Google Scholar]
  10. Mori, G., Xiaofeng, R., Efros, A. A., and Malik, J.
    2004]. Recovering human body configurations: combining segmentation and recognition. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, 2, 326–333. https://doi.org/10.1109/CVPR.2004.1315182
    [Google Scholar]
  11. Prince, C. M., and Chitale, J.
    [2008]. Core Image Analysis : Reliable Pay Estimation in Thin-Bedded Reservoir Units. Image Processing, 1–6.
    [Google Scholar]
  12. Ronneberger, O., Fischer, P., and Brox, T.
    [2015]. U-Net: Convolutional Networks for Biomedical Image Segmentation. Retrieved from http://arxiv.org/abs/1505.04597
    [Google Scholar]
  13. Thomas, A., Rider, M., Curtis, A., and MacArthur, A.
    [2011]. Automated lithology extraction from core photographs. First Break, 29(6), 103–109. Retrieved from https://www.geos.ed.ac.uk/homes/acurtis/Thomas_etal_FirstBreak_2011.pdf
    [Google Scholar]
  14. Travis, O. E.
    [2006]. A guide to NumPy. USA: Trelgol Publishing.
    [Google Scholar]
  15. Tremeau, A., and Colantoni, P.
    [2000]. Regions adjacency graph applied to color image segmentation. IEEE Transactions on Image Processing, 9 (4), 735–744. https://doi.org/10.1109/83.841950
    [Google Scholar]
  16. Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., and Smeulders, A. W. M.
    [2013]. Selective Search for Object Recognition. International Journal of Computer Vision, 104(2), 154–171. https://doi.org/10.1007/s11263-013-0620-5
    [Google Scholar]
  17. van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., and Yu, T.
    [2014]. scikit-image: image processing in Python. PeerJ, 2, e453. https://doi.org/10.7717/peerj.453
    [Google Scholar]
  18. Van Rossum, G., and Drake, F. L.
    [2011]. The Python language reference manual : for Python version 3.2. Network Theory Ltd.
    [Google Scholar]
  19. Vedaldi, A., and Soatto, S.
    [2008]. Quick Shift and Kernel Methods for Mode Seeking. In Computer Vision – ECCV 2008 (pp. 705–718). https://doi.org/10.1007/978-3-540-88693-8_52
    [Google Scholar]
  20. Wieling, I. S.
    [2013]. Facies and permeability prediction based on analysis of core images. Retrieved from https://repository.tudelft.nl/islandora/object/uuid%3A9b6bd4b0-1001-4d9b-a6eb-7761bc3b2309
    [Google Scholar]
  21. Xiaofeng, R., and Malik, J.
    [2003]. Learning a classification model for segmentation. Proceedings Ninth IEEE International Conference on Computer Vision, 10–17 vol.1. https://doi.org/10.1109/ICCV.2003.1238308
    [Google Scholar]
/content/papers/10.3997/2214-4609.201953061
Loading
/content/papers/10.3997/2214-4609.201953061
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error