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AAPG Bulletin, V.
Methods for identification of isolated carbonate buildups from seismic reflection data
1Shell International Exploration and Production B.V., Rijswijk, The Netherlands; present address: Department of Earth Sciences, Royal Holloway University of London, London, United Kingdom; firstname.lastname@example.org
2Shell International Exploration and Production Inc., 3737 Bellaire Boulevard, P.O. Box 481, Houston, Texas; present address: Sarawak Shell Berhad, Kuala Lumpur, Malaysia; email@example.com
3Shell International Exploration and Production Inc., 3737 Bellaire Boulevard, P.O. Box 481, Houston, Texas; firstname.lastname@example.org
4Department of Earth Sciences, Royal Holloway University of London, London, United Kingdom; email@example.com
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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