Seismic similarity analysis through self-organising maps

Oscar Rondon; Rafael Banchs

doi:10.1071/EG07018

ISSN: 0812-3985
E-ISSN: 1834-7533

Seismic similarity analysis through self-organising maps
Authors Oscar Rondon^a,c and Rafael Banchs^b
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^a Snowden Group, 87 Colin Street, West Perth, Perth, WA 6005, Australia. ^b Signal Processing and Communications Department, Universitat Politècnica de Catalunya Campus Nord, D4-100, Barcelona 08034, Spain. ^c Corresponding author. Email: [email protected]
Publisher: Australian Society of Exploration Geophysicists
Source: Exploration Geophysics, Volume 38, Issue 3, Sep 2007, p. 184 - 188
DOI: https://doi.org/10.1071/EG07018
- Received: 27 Oct 2006
- Accepted: 30 Jul 2007
- Published online: 01 Sep 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.

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Seismic similarity analysis through self-organising maps

Exploration Geophysics 38, 184 (2007); https://doi.org/10.1071/EG07018

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Article Type: Research Article

Keyword(s): classification; pattern recognition; seismic attributes; seismic similarity; self-organising maps

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Seismic similarity analysis through self-organising maps

Abstract

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