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Abstract

Summary

In seismic interpretation, a big amount of data has to be handled to segment the data cube in zones and faults. In the conventional method, inlines, crosslines and seismic sections are interpreted to divide the geological zones on seismic reflectors and on seismic discontinuities. This segmentation is often guided by seismic attributes, wells and further geological information.

The other approach of seismic interpretation is dividing seismic data by algorithms. One popular method to achieve an automatic segmentation is clustering of seismic attributes. There are several clustering algorithms available in all different kinds of scientific disciplines. Some are also already used in seismic interpretation. To get an overview of clustering algorithms and to understand the different kinds of algorithms a research study was done. Therefore, multiple algorithms were classified in a matrix and a workflow was created to test various algorithms on different synthetic 3D seismic data models and subsequently a test environment was founded to understand algorithms to use them for automatic or semiautomatic interpretation of seismic data.

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/content/papers/10.3997/2214-4609.201700922
2017-06-12
2024-03-28
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References

  1. Amtmann, J., Eichkitz, C.G. and Schreilechner, M.G.
    [2012] Seismic attribute database for time effective literature research. 74th EAGE Conference & Exhibition, Extended Abstracts, B020.
    [Google Scholar]
  2. Barnes, A.E., Laughlin, K.J.
    , [2002], Investigation of methods for unsupervised classification of seismic data, 72nd SEG Annual Meeting, SEG Expanded Abstracts, 21, 2221–2224.
    [Google Scholar]
  3. Kohonen, T.
    , [1990], The Self-Organizing Map, Proceedings of the IEEE, 78, 9, 1464–1480.
    [Google Scholar]
  4. MacQueen, J.B.
    , [1967], Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, 1, 281–297.
    [Google Scholar]
  5. Paasche, H., Tronicke, J.
    , [2007], Cooperative inversion of 2D geophysical data sets: A zonal approach based on fuzzy c-means cluster analysis, Geophysics, 72, 3, 35–39.
    [Google Scholar]
  6. Roy, A. and Marfurt, K.J.
    [2011] Cluster assisted 3D and 2D unsupervised seismic facies analysis, an example from the Barnett Shale Formation in the Fort Worth Basin, Texas. SEG Annual Meeting, San Antonio.
    [Google Scholar]
  7. Sabeti, H. and Javaherian, A.
    [2009] Seismic Facies Analysis Based on K-means Clustering Algorithm Using 3D Seismic Attributes. First International Petroleum Conference & Exhibition, EAGE, Shiraz, A26.
    [Google Scholar]
  8. Strecker, U., Uden, R.
    , [2002], Data mining of 3D poststack seismic attribute volumes using Kohonen self-organizing maps, The Leading Edge, 21, 10, 1032–1037.
    [Google Scholar]
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