Exploration Geophysics Exploration Geophysics Society
Journal of the Australian Society of Exploration Geophysicists
RESEARCH ARTICLE

Seismic similarity analysis through self-organising maps

Oscar Rondon 1 3 Rafael Banchs 2
+ Author Affiliations
- Author Affiliations

1 Snowden Group, 87 Colin Street, West Perth, Perth, WA 6005, Australia.

2 Signal Processing and Communications Department, Universitat Politècnica de Catalunya Campus Nord, D4-100, Barcelona 08034, Spain.

3 Corresponding author. Email: orondon@snowdengroup.com

Exploration Geophysics 38(3) 184-188 https://doi.org/10.1071/EG07018
Submitted: 27 October 2006  Accepted: 30 July 2007   Published: 19 September 2007

Abstract

In this work we present a methodology for extracting valuable information from several seismic attributes by computing seismic similarity values through a pattern recognition approach, which is based on self-organising maps. This methodology allows for identifying regions with seismic properties similar to a pre-defined reference location of interest, for instance, a good well producer or a dry well; and it can be used during the exploratory phase when only limited and scarce well information is available. The methodology we propose improves the classical seismic similarity analysis and can be used on two-dimensional seismic maps or three-dimensional seismic volumes for frontier exploration, i.e. where there are scarce or limited well data but much seismic information. Using two case studies, we show how the proposed method constitutes a valuable tool for exploration geophysics and reservoir characterisation.

Key words: pattern recognition, self-organising maps, seismic attributes, seismic similarity, classification.


Acknowledgments

We would like to thank to Dr Juan Jimenez for his valuable help with the geological description of the regions analysed in the case studies as well as the interpretation of some of the results.


References

Becker, S., 1991, Unsupervised learning procedures for neural networks: International Journal of Neural Systems 2, 17–33.
CrossRef |

Coléou, T. R., Poupon, M., and Azbel, K., 2003, Unsupervised seismic facies classification: a review and comparison of techniques and implementation: The Leading Edge 22, 942–953.
CrossRef |

Haykin S. , 1994, Neural Networks: a Comprehensive Foundation, MacMillan.

Henriquez N. , Castro S. , and Perez M. , 2002, Successful application of pseudo properties estimation and seismic similarity for delineation of stratigraphic traps in Western Venezuela: Memoirs of the XI Venezuelan Geophysical Congress.

Kohonen, T., 1990, The self-organising map: Proceedings of the IEEE 78, 1464–1480.
CrossRef |

Michelena, R., González, E., and Capello, M., 1998, Similarity analysis: a new tool to summarize seismic attributes information: The Leading Edge 17, 545–548.
CrossRef |

Strecker, U., and Uden, K., 2002, Data mining of 3D post-stack seismic attribute volumes using Kohonen self-organising maps: The Leading Edge 21, 1032–1037.
CrossRef |



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