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    Methods for identification of isolated carbonate build-upsfrom seismic reflection data

    Access Status
    Fulltext not available
    Authors
    Burgess, P.
    Winefield, P.
    Minzoni, M.
    Elders, Chris
    Date
    2013
    Type
    Journal Article
    
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    Citation
    Burgess, P. and Winefield, P. and Minzoni, M. and Elders, C. 2013. Methods for identification of isolated carbonate build-upsfrom seismic reflection data. Bulletin of the American Association of Petroleum Geologists. 97 (7): pp. 1071-1098.
    Source Title
    Bulletin of the American Association of Petroleum Geologists
    ISSN
    00027464
    URI
    http://hdl.handle.net/20.500.11937/24502
    Collection
    • Curtin Research Publications
    Abstract

    Isolated carbonate buildups (ICBs) are commonly attractive exploration targets. However, identifying ICBs based only on seismic data can be difficult for a variety of reasons. These include poor-quality two-dimensional data and a basic similarity between ICBs and other features such as volcanoes, erosional remnants, and tilted fault blocks. To address these difficulties and develop reliable methods to identify ICBs, 234 seismic images were analyzed. The images included proven ICBs and other features, such as folds, volcanoes, and basement highs, which may appear similar to ICBs when imaged in seismic data. From this analysis, 18 identification criteria were derived to distinguish ICBs from non-ICB features. These criteria can be grouped into four categories: regional constraints, analysis of basic seismic geometries, analysis of geophysical details, and finer-scale seismic geometries. Systematically assessing the criteria is useful because it requires critical evaluation of the evidence present in the available data, working from the large-scale regional geology to the fine details of seismic response. It is also useful to summarize the criteria as a numerical score to facilitate comparison between different examples and different classes of ICBs and non-ICBs. Our analysis of scores of different classes of features suggests that the criteria do have some discriminatory power, but significant challenges remain.

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