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Abstract

Summary

Accurate delineation of salt bodies is one of the major tasks of hydrocarbon exploration and production from 3D seismic surveying. With the increasing demand of high-resolution seismic interpretation, the size of 3D seismic volumes as well as the number of available seismic attributes has been rapidly rising, which adds the difficulties for interpreters to examine and interpret every vertical line and time slice in a seismic volume. Various machine learning techniques have been introduced from the field of image/video processing to help address this limitation; however, little effort has been devoted to fair comparisons between these techniques. This study implements six commonly-used classification techniques and compares their capabilities for salt-boundary detection, including logistic regression, decision tree, random forest, support vector machine, artificial neural network, and k-means clustering, through applications to the F3 seismic dataset of multiple salt bodies over the Netherlands North Sea. The good match between the detected salt boundaries and the original seismic images indicates that based on well-selected attributes, all six classification techniques are capable of providing reliable salt detection from 3D seismic data to assist structural framework modelling in the presence of salt domes.

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/content/papers/10.3997/2214-4609.201700919
2017-06-12
2020-01-20
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References

  1. Barnes, A. E., and Laughlin, K. J.
    [2002] Investigation of methods for unsupervised classification of seismic data:72nd SEG Annual International Meeting Expanded Abstracts, 2221–2224.
    [Google Scholar]
  2. Di, H., and Gao, D.
    [2014] Gray-level transformation and Canny edge detection for 3D seismic discontinuity enhancement. Computers & Geosciences, 72, 192–200.
    [Google Scholar]
  3. [2016] Improved seismic texture analysis based on non-linear gray-level transformation:86th SEG Technical Program Expanded Abstracts, 2093–2097.
    [Google Scholar]
  4. Di, H., Shafiq, M. A., AlRegib, G.
    [2017] Multi-attribute k-means cluster analysis for salt boundary detection. 79th EAGE Conference & Exhibition, Extended Abstracts
    [Google Scholar]
  5. Eichkitz, C. G., Amtmann, J. and Schreilechner, M. G.
    [2013] Calculation of gray level co-occurrence matrix-based seismic attributes in three dimensions. Computers & Geosciences, 60, 176–183.
    [Google Scholar]
  6. Gao, D.
    [2003] Volume texture extraction for 3D seismic visualization and interpretation. Geophysics, 68, 1294–1302.
    [Google Scholar]
  7. Haukas, J., Ravndal, O. R., Fotland, B. H., Bounaim, A., and Sonneland, L.
    [2013] Automated salt body extraction from seismic data using the level set method. First Break, 31, 35–42.
    [Google Scholar]
  8. Lomask, J., Clapp, R. G. and Biondi, B.
    [2007] Application of image segmentation to tracking 3D salt boundaries. Geophysics, 72, P47–P56
    [Google Scholar]
  9. Shi, J., and Malik, J.
    [2000] Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 888–905
    [Google Scholar]
  10. Shafiq, M. A., Wang, Z. and AlRegib, G.
    [2015] Seismic interpretation of migrated data using edge-based geodesic active contours. GlobalSIP, 596–600.
    [Google Scholar]
  11. Shafiq, M. A., Alshawi, T., Long, Z. and AlRegib, G.
    [2016] SAISI: A new seismic attribute for salt dome detection. ICASSP, 1876–1880.
    [Google Scholar]
  12. Wang, Z., Hegazy, T., Long, Z., and AlRegib, G.
    [2015] Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learning. Geophysics, 80, WD101–WD116.
    [Google Scholar]
  13. Wu, X.
    [2016] Methods to compute salt likelihoods and extract salt boundaries from 3D seismic images. Geophysics, 81, IM119–IM126.
    [Google Scholar]
  14. ZhangY., and Halpert, A. D.
    [2012] Enhanced interpreter-aided salt boundary extraction using shape deformation. 82nd SEG Annual International Meeting Expanded Abstracts, 1–5.
    [Google Scholar]
  15. Zhao, T., Jayaram, V., Roy, A., and Marfurt, K. J.
    [2015] A comparison of classification techniques for seismic facies recognition. Interpretation, 3, SAE29–SAE58.
    [Google Scholar]
  16. Zheng, Z., Kavousi, P., Di, H.
    [2014] Multi-attributes and neural network-based fault detection in 3D seismic interpretation. Advanced Materials Research, 838–841, 1497–1502.
    [Google Scholar]
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