Changes

  | part    = Predicting the occurrence of oil and gas traps

  | part    = Predicting the occurrence of oil and gas traps

  | chapter = Applied paleontology

  | chapter = Applied paleontology

  | frompg  = 17-1

  | frompg  = 17-38

  | topg    = 17-65

  | topg    = 17-38

  | author  = Robert L. Fleisher, H. Richard Lane

  | author  = Robert L. Fleisher, H. Richard Lane

  | link    = http://archives.datapages.com/data/specpubs/beaumont/ch17/ch17.htm

  | link    = http://archives.datapages.com/data/specpubs/beaumont/ch17/ch17.htm

  | isbn    = 0-89181-602-X

  | isbn    = 0-89181-602-X

}}

}}

Benthic foraminifera have been used as paleoenvironmental indicators in the petroleum industry for forty years—most particularly as a basis for subjective estimates of paleobathymetry based on the presumed water depth range of species in the samples (see [[Paleobathymetry]]). Recent studies have shown that computer-based quantitative techniques such as clustering, principal component analysis, and discriminate analysis result in the following advantages:

[[Benthic]] [[foraminifera]] have been used as [[Paleoenvironmental analysis|paleoenvironmental]] indicators in the [[petroleum]] industry for forty years—most particularly as a basis for subjective estimates of [[paleobathymetry]] based on the presumed water depth range of species in the samples. Recent studies have shown that computer-based quantitative techniques such as clustering, principal component analysis, and discriminant analysis result in the following advantages:

* Permit the use of large data sets

* Permit the use of large data sets

* Increase the objectivity, reliability, and reproducibility of interpretations

* Increase the objectivity, reliability, and reproducibility of interpretations

* Clarify the definition of significant assemblages

* Clarify the definition of significant [[Fossil assemblage|assemblages]]

==Cluster analysis==

==Cluster analysis==

[[file:applied-paleontology_fig17-22.png|300px|thumb|{{figure number|1}}See text for explanation.]]

[[file:applied-paleontology_fig17-22.png|300px|thumb|{{figure number|1}}Example of weighted interfossil distances for Neogene benthic foraminifera of offshore California.]]

One of the most popular methods of recognizing biofacies assemblages is cluster analysis. This method arranges the species into a hierarchical classification called a ''dendrogram''. Dendrograms are constructed from the statistical distance or similarity between samples, based on their species composition. The species having the highest degree of similarity are clustered first, then others in successive order, until all species are paired into clusters. The results of the cluster analysis show faunal groupings, or ''biofacies'', which are often characteristic of specific environments. [[:file:applied-paleontology_fig17-22.png|Figure 1]] is an example of weighted interfossil distances for Neogene benthic foraminifera of offshore California.

One of the most popular methods of recognizing [[Fossil assemblage|biofacies assemblages]] is cluster analysis. This method arranges the species into a hierarchical classification called a ''dendrogram''. Dendrograms are constructed from the statistical distance or similarity between samples, based on their species composition. The species having the highest degree of similarity are clustered first, then others in successive order, until all species are paired into clusters. The results of the cluster analysis show faunal groupings, or ''biofacies'', which are often characteristic of specific environments. [[:file:applied-paleontology_fig17-22.png|Figure 1]] is an example of weighted interfossil distances for [[Neogene]] benthic foraminifera of offshore California.

==Principal component analysis==

==Principal component analysis==

The patterns, or ''factor plots'', displayed in principal component analysis represent groupings of species based on the similarity or dissimilarity of their distribution. These groupings may represent various environments and can be integrated with other geologic data to determine or evaluate specific paleoenvironmental zones or settings.

The patterns, or ''factor plots'', displayed in principal component analysis represent groupings of species based on the similarity or dissimilarity of their distribution. These groupings may represent various environments and can be integrated with other geologic data to determine or evaluate specific paleoenvironmental zones or settings.

==Discriminate analysis==

==Discriminant analysis==

In discriminate analysis, paleoenvironmental zones are established and then tested against known environmental models to check the level of reliability in recognizing these biofacies. The results indicate which biofacies are distinct and statistically recognizable. The biofacies so determined can be used to interpret the paleoenvironment of well sections, cores, or outcrop sections.

In discriminant analysis, paleoenvironmental zones are established and then tested against known environmental models to check the level of reliability in recognizing these biofacies. The results indicate which biofacies are distinct and statistically recognizable. The biofacies so determined can be used to interpret the paleoenvironment of well sections, cores, or [http://www.merriam-webster.com/dictionary/outcrop outcrop] sections.

==See also==

==See also==

* [[Palynofacies and kerogen analysis]]

* [[Palynofacies and kerogen analysis]]

* [[Thermal maturation]]

* [[Thermal maturation]]

* [[Sequence stratigraphy]]

* [[Biostratigraphy in sequence stratigraphy]]

 

* [http://www.eso.org/sci/software/esomidas//doc/user/98NOV/volb/node214.html Discriminant analysis overview]

==External links==

==External links==

[[Category:Predicting the occurrence of oil and gas traps]]  

[[Category:Predicting the occurrence of oil and gas traps]]  

[[Category:Applied paleontology]]

[[Category:Applied paleontology]]