Advertisement

Automatic Fault Extraction Using Artificial Ants

  • Stein Inge Pedersen
  • Thorleif Skov
  • Trygve Randen
  • Lars Sønneland
Part of the Mathematics in Industry book series (MATHINDUSTRY, volume 7)

Summary

A high-level fault interpretation workflow using automatically extracted surfaces is presented. The first step of the workflow is to generate a fault attribute that enhances the discontinuities in the seismic data. Fault-like surfaces are then extracted using an algorithm called ant tracking. The surfaces are then loaded into an analysis tool where the interpreter, by interactively working with the surfaces, decides on the final interpretation. The interpreter works on two levels in the analysis tool. Firstly, on the system level, where the fault surfaces are split into separate systems according to their strikes. Faults that are created at the same time period typically form a fault system. This separation is geologically meaningful and gives the interpreter an overview of the structural history of the area. Secondly, the interpreter groups and modifies individual surfaces within each fault system to form the final interpretation. The workflow is demonstrated as a case study of two fields offshore mid Norway.

Keywords

Short Path Seismic Data Fault System Swarm Intelligence Seismic Section 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    E. Bonbeau and G. Theraulaz (2000) Swarm smarts. Scientific American, 51–64.Google Scholar
  2. 2.
    H.G. Borgos, T. Skov, T. Randen, and L. Sønneland (2003) Automated geometry extraction from 3d seismic data. Expanded Abstr., Int. Mtg., Soc. Exploration Geophys., 1541–1544.Google Scholar
  3. 3.
    G. Mandl (1998) Mechanics of techtonic faulting. Elsevier.Google Scholar
  4. 4.
    S.I. Pedersen, T. Randen, L. Sønneland, and Ø. Steen (2002) Automatic fault extraction using artificial ants. Expanded Abstr., Int. Mtg., Soc. Exploration Geophys., 512–515.Google Scholar
  5. 5.
    T. Randen, E. Monsen, C. Signer, A. Abrahamsen, J.O. Hansen, T. Sater, J. Schlaf, and L. Sønneland (2000) Three-dimensional texture attributes for seismic data analysis. Expanded Abstr., Int. Mtg., Soc. Exploration Geophys.Google Scholar
  6. 6.
    T. Randen, S.I. Pedersen, and L. Sønneland (2001) Automatic extraction of fault surfaces from three-dimensional seismic data. Expanded Abstr., Int. Mtg., Soc. Exploration Geophys., 551–554.Google Scholar
  7. 7.
    T. Randen, L. Sønneland, A. Carrillat, T.S. Valen, T. Skov, S.I. Pedersen, B. Rafaelsen, and G. Elvebakk (2003) Preconditioning for optimal 3d stratigraphical and structural inversion. Extended Abstr., Eur. Assoc. Expl. Geophys.Google Scholar
  8. 8.
    T. Skov, S.I. Pedersen, T.S. Valen, P. Fayemendy, A. Grønlie, J.O. Hansen, A. Hetlelid, T. Iversen, T. Randen, and L. Sønneland (2003) Fault system analysis using a new interpretation paradigm. Extended Abstr., Eur. Assoc. Expl. Geophys.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Stein Inge Pedersen
    • 1
  • Thorleif Skov
    • 1
  • Trygve Randen
    • 1
  • Lars Sønneland
    • 1
  1. 1.Schlumberger Stavanger ResearchStavangerNorway

Personalised recommendations