Refining palaeoenvironmental analysis using integrated quantitative granulometry and palynology
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Stukins et al_Final MS 2nd.pdf
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Issue date
27/04/2017Submitted date
2017-05-11Subject Terms
palaeoenvironmental analysisquantitative palynology
quantitative granulometry
Lajas Formation of the Neuquén Basin
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Show full item recordAbstract
Accurate palaeoenvironmental analysis is at the heart of producing reliable interpretations and depositional models. This study demonstrates a multivariate statistical approach to facies analysis based on relationships between grain size and quantitative palynology. Our methodology has the advantage that it can be used on small amounts of sample, such as core or well cuttings, as the basis for facies analysis. Proof of concept studies involving the collection of grain-size and palynological datasets from well-exposed outcrops of the Middle Jurassic, Lajas Formation of the Neuquén Basin, Argentina, demonstrate that canonical correspondence analysis can be used to consistently recognize facies and aid in the determination of depositional environments. This study demonstrates the link between depositional facies, grain-size distribution, palynomorph hydrodynamics and assemblage taphonomy of palynomorphs. This knowledge can be transferred into a semi-automated statistical facies prediction technique for the subsurface in complex depositional settings, particularly when calibrated against conventional sedimentary facies analysis.Citation
Stukins, Stephen; McIlroy, Duncan; W. Jolley, David (2017) "Refining palaeoenvironmental analysis using integrated quantitative granulometry and palynology." Petroleum Geoscience, 23(4), pp. 395-402Publisher
The Geological SocietyJournal
Petroleum GeoscienceAdditional Links
https://doi.org/10.6084/m9.figshare.c.3745481.v1Type
Journal ArticleItem Description
The attached article is the accepted manuscript for this article. You are advised to consult the publisher's version if you wish to cite from the article.NHM Repository
ISSN
1354-0793EISSN
2041-496Xae974a485f413a2113503eed53cd6c53
10.1144/petgeo2016-073
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