1887

Abstract

Summary

Fault interpretation prior to seismic analysis is essential to constraint the horizon interpretation. This process, if undertaken manually, is tedious and time consuming. Hence, many authors created attributes based on coherency, curvature or spectral decomposition to detect faults and extract them from the seismic. Yet, even the most recent methods based on these attributes and machine learning does not yield accomplished results yet. In this work, we present a novel method to automatically detect and extract faults from a seismic volume through an optimized processing. Our workflow is based on the Fault Plane attribute obtained from the Variance, the creation of a Thinning volume (representing the skeleton of the deformation) and, finally, the extraction and creation of 3D fault patches. Our test on a 650 km2 block from the Carnarvon basin shows that the method allows intercepting most of the seismic faults, with coherent positions, and fairly capturing the structural complexities of fault interactions (e.g. relays, polygonal faults). Combined with interactive editing, we believe that our method can be inserted in a very fast and efficient workflow, where the interpreter can control the detection parameters and refine locally the fault picking.

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/content/papers/10.3997/2214-4609.201901176
2019-06-03
2024-03-28
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References

  1. Bahorich, M., Lopez, J., Haskell, N., Nissen, S., Poole, A.
    , 1995. Stratigraphic and structural interpretation with 3-D coherence, in: SEG Technical Program Expanded Abstracts 1995, SEG Technical Program Expanded Abstracts. Society of Exploration Geophysicists, 97–100.
    [Google Scholar]
  2. Boyd, R., Williamson, P., Haq, B.U.
    , 2009. Seismic Stratigraphy and Passive-Margin Evolution of the Southern Exmouth Plateau. In: Sequence Stratigraphy and Facies Associations. John Wiley & Sons, Ltd, 579–603.
    [Google Scholar]
  3. Chopra, S., Marfurt, K.J.
    , 2007. Volumetric curvature attributes add value to 3D seismic data interpretation. Lead. Edge26, 856–867.
    [Google Scholar]
  4. Gibson, D., Spann, M., Turner, J.
    , 2003. Automatic Fault Detection for 3D Seismic Data 10.
    [Google Scholar]
  5. Guitton, A., Wang, H., Trainor-Guitton, W.
    , 2017. Statistical imaging of faults in 3D seismic volumes using a machine learning approach. In: SEG Technical Program Expanded Abstracts 2017, SEG Technical Program Expanded Abstracts. Society of Exploration Geophysicists, 2045–2049.
    [Google Scholar]
  6. Guo, B., Li, L., Luo, Y.
    , 2018. A new method for automatic seismic fault detection using convolutional neural network. In: SEG Technical Program Expanded Abstracts 2018, SEG Technical Program Expanded Abstracts. Society of Exploration Geophysicists, pp. 1951–1955.
    [Google Scholar]
  7. Hale, D.
    , 2013. Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images. Geophysics78, O33–O43.
    [Google Scholar]
  8. Ma, Y., Ji, X., BenHassan, N., Luo, Y.
    , 2018. A deep-learning method for automatic fault detection. In: SEG Technical Program Expanded Abstracts 2018, SEG Technical Program Expanded Abstracts. Society of Exploration Geophysicists, 1941–1945.
    [Google Scholar]
  9. Wu, X., Fomel, S.
    , 2018. Automatic fault interpretation using optimal surface voting. In: SEG Technical Program Expanded Abstracts 2018, SEG Technical Program Expanded Abstracts. Society of Exploration Geophysicists, 1639–1643.
    [Google Scholar]
  10. Wu, X., Shi, Y., Fomel, S., Liang, L.
    , 2018. Convolutional neural networks for fault interpretation in seismic images. In: SEG Technical Program Expanded Abstracts 2018, SEG Technical Program Expanded Abstracts. Society of Exploration Geophysicists, 1946–1950.
    [Google Scholar]
  11. Zhao, T., Mukhopadhyay, P.
    , 2018. A fault-detection workflow using deep learning and image processing, in: SEG Technical Program Expanded Abstracts 2018, SEG Technical Program Expanded Abstracts. Society of Exploration Geophysicists, 1966–1970.
    [Google Scholar]
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