A Comparison between Statistical Analysis and Grey Model Analysis on Assessed Parameters of Petroleum Potential from Organic Materials

Hsien-Tsung Lee

doi:doi:10.11648/j.jenr.20150401.12

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A Comparison between Statistical Analysis and Grey Model Analysis on Assessed Parameters of Petroleum Potential from Organic Materials

Hsien-Tsung Lee

Published in Journal of Energy and Natural Resources (Volume 4, Issue 1)

Received: 29 December 2014 Accepted: 20 January 2015 Published: 30 January 2015

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Abstract

This study aims to investigate the impact of changes in the Tmax and Ro% on the assessed parameters (S1, S2, S1+S2, HI, QI, BI, PI, TOC) of petroleum potential of organic materials. The samples studied include coals and coaly shales of Mushan Formation, Shihti Formation and Nanchuang Formation in NW Taiwan, coals and an oil shale from Mainland China, the well-drilled chip samples from NW Australia, in addition to the data of samples were included from literatures. This work will get on the detecting data of 10 parameters (S1, S2, S1+S2, TOC, HI, QI, BI, PI, Ro%, Tmax) and progressing statistical analysis, and focus the study on comparison between grey forecast of grey relational grade and regression model forecast. The results from statistical analysis (include temperature-treated samples were individually subjected to Rock-Eval analysis) of the all parameters data for all samples in this research project, not only be executed a linear regression, curve regression between any two parameters, and multivariate regression, but also be carried on the forecast of grey correlation grade of grey theory (include grey relational generating (Nominal-the-better-:Ro%; Larger-the-better-: Tmax, HI, QI, BI, S2, S1+ S2, S1; smaller-the-better-: TOC, PI) and globalization grey relational grade). So far, obtain roughly the consistency of results from two type predictive analysis. The constructed HI, QI and BI bands were broad at low maturities and gradually narrowed with increasing thermal maturity. The petroleum generation potential is completely exhausted at a vitrinite reflectance of 2.0-2.2% or a Tmax of 510-520°C. An increase in HI and QI suggests extra petroleum potential related to changes in the structure of the organic material. A decline in BI signifies the start of the oil expulsion window and occurs within the vitrinite reflectance range 0.75-1.05 % or a Tmax of 440-455 oC. Furthermore, petroleum potential can be divided into four different parts based on the cross-plot of HI vs. %Ro. The area with the highest petroleum potential is located in sectionⅡ with %Ro=0.6-1.0%, and HI>100. Oil generation potential is rapidly exhausted at section Ⅲ with %Ro >1.0%. This result is in accordance with the regression curve of HI and QI with %Ro based on 97 samples with %Ro=1.0~5.6%.

Published in	Journal of Energy and Natural Resources (Volume 4, Issue 1)
DOI	10.11648/j.jenr.20150401.12
Page(s)	5-26
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Vitrinite Reflectance (Ro %), Grey Relational Analysis, Grey Model, Rock-Eval Pyrolysis, Petroleum Potential, Statistical Analysis

References

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Hsien-Tsung Lee. (2015). A Comparison between Statistical Analysis and Grey Model Analysis on Assessed Parameters of Petroleum Potential from Organic Materials. Journal of Energy and Natural Resources, 4(1), 5-26. https://doi.org/10.11648/j.jenr.20150401.12

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Hsien-Tsung Lee. A Comparison between Statistical Analysis and Grey Model Analysis on Assessed Parameters of Petroleum Potential from Organic Materials. J. Energy Nat. Resour. 2015, 4(1), 5-26. doi: 10.11648/j.jenr.20150401.12

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Hsien-Tsung Lee. A Comparison between Statistical Analysis and Grey Model Analysis on Assessed Parameters of Petroleum Potential from Organic Materials. J Energy Nat Resour. 2015;4(1):5-26. doi: 10.11648/j.jenr.20150401.12

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@article{10.11648/j.jenr.20150401.12,
  author = {Hsien-Tsung Lee},
  title = {A Comparison between Statistical Analysis and Grey Model Analysis on Assessed Parameters of Petroleum Potential from Organic Materials},
  journal = {Journal of Energy and Natural Resources},
  volume = {4},
  number = {1},
  pages = {5-26},
  doi = {10.11648/j.jenr.20150401.12},
  url = {https://doi.org/10.11648/j.jenr.20150401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jenr.20150401.12},
  abstract = {This study aims to investigate the impact of changes in the Tmax  and Ro% on the assessed parameters (S1, S2, S1+S2, HI, QI, BI, PI, TOC) of petroleum potential of organic materials. The samples studied include coals and coaly shales of Mushan Formation, Shihti Formation  and Nanchuang Formation in NW Taiwan, coals and an oil shale from Mainland China, the well-drilled chip samples from NW Australia, in addition to the data of samples were included from literatures. This work will get on the detecting data of 10 parameters (S1, S2, S1+S2, TOC, HI, QI, BI, PI, Ro%, Tmax) and progressing statistical analysis, and focus the study on comparison between grey forecast of grey relational grade and regression model forecast. The results from statistical analysis (include temperature-treated samples were individually subjected to Rock-Eval analysis) of the all parameters data for all samples in this research project, not only be executed a linear regression, curve regression between any two parameters, and multivariate regression, but also be carried on the forecast of grey correlation grade of grey theory (include grey relational generating (Nominal-the-better-:Ro%; Larger-the-better-: Tmax, HI, QI, BI, S2, S1+ S2, S1; smaller-the-better-: TOC, PI) and globalization grey relational grade). So far, obtain roughly the consistency of results from two type predictive analysis. The constructed HI, QI and BI bands were broad at low maturities and gradually narrowed with increasing thermal maturity. The petroleum generation potential is completely exhausted at a vitrinite reflectance of 2.0-2.2% or a Tmax of 510-520°C. An increase in HI and QI suggests extra petroleum potential related to changes in the structure of the organic material. A decline in BI signifies the start of the oil expulsion window and occurs within the vitrinite reflectance range 0.75-1.05 % or a Tmax of 440-455 oC. Furthermore, petroleum potential can be divided into four different parts based on the cross-plot of HI vs. %Ro. The area with the highest petroleum potential is located in sectionⅡ with %Ro=0.6-1.0%, and HI>100. Oil generation potential is rapidly exhausted at section Ⅲ with %Ro >1.0%. This result is in accordance with the regression curve of HI and QI with %Ro based on 97 samples with %Ro=1.0~5.6%.},
 year = {2015}
}

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TY  - JOUR
T1  - A Comparison between Statistical Analysis and Grey Model Analysis on Assessed Parameters of Petroleum Potential from Organic Materials
AU  - Hsien-Tsung Lee
Y1  - 2015/01/30
PY  - 2015
N1  - https://doi.org/10.11648/j.jenr.20150401.12
DO  - 10.11648/j.jenr.20150401.12
T2  - Journal of Energy and Natural Resources
JF  - Journal of Energy and Natural Resources
JO  - Journal of Energy and Natural Resources
SP  - 5
EP  - 26
PB  - Science Publishing Group
SN  - 2330-7404
UR  - https://doi.org/10.11648/j.jenr.20150401.12
AB  - This study aims to investigate the impact of changes in the Tmax  and Ro% on the assessed parameters (S1, S2, S1+S2, HI, QI, BI, PI, TOC) of petroleum potential of organic materials. The samples studied include coals and coaly shales of Mushan Formation, Shihti Formation  and Nanchuang Formation in NW Taiwan, coals and an oil shale from Mainland China, the well-drilled chip samples from NW Australia, in addition to the data of samples were included from literatures. This work will get on the detecting data of 10 parameters (S1, S2, S1+S2, TOC, HI, QI, BI, PI, Ro%, Tmax) and progressing statistical analysis, and focus the study on comparison between grey forecast of grey relational grade and regression model forecast. The results from statistical analysis (include temperature-treated samples were individually subjected to Rock-Eval analysis) of the all parameters data for all samples in this research project, not only be executed a linear regression, curve regression between any two parameters, and multivariate regression, but also be carried on the forecast of grey correlation grade of grey theory (include grey relational generating (Nominal-the-better-:Ro%; Larger-the-better-: Tmax, HI, QI, BI, S2, S1+ S2, S1; smaller-the-better-: TOC, PI) and globalization grey relational grade). So far, obtain roughly the consistency of results from two type predictive analysis. The constructed HI, QI and BI bands were broad at low maturities and gradually narrowed with increasing thermal maturity. The petroleum generation potential is completely exhausted at a vitrinite reflectance of 2.0-2.2% or a Tmax of 510-520°C. An increase in HI and QI suggests extra petroleum potential related to changes in the structure of the organic material. A decline in BI signifies the start of the oil expulsion window and occurs within the vitrinite reflectance range 0.75-1.05 % or a Tmax of 440-455 oC. Furthermore, petroleum potential can be divided into four different parts based on the cross-plot of HI vs. %Ro. The area with the highest petroleum potential is located in sectionⅡ with %Ro=0.6-1.0%, and HI>100. Oil generation potential is rapidly exhausted at section Ⅲ with %Ro >1.0%. This result is in accordance with the regression curve of HI and QI with %Ro based on 97 samples with %Ro=1.0~5.6%.
VL  - 4
IS  - 1
ER  -

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Hsien-Tsung Lee

Department of Electrical and Information Technology, NanKaiUniversity of Technology, Nan Tou County, Taiwan

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Hsien-Tsung Lee. (2015). A Comparison between Statistical Analysis and Grey Model Analysis on Assessed Parameters of Petroleum Potential from Organic Materials. Journal of Energy and Natural Resources, 4(1), 5-26. https://doi.org/10.11648/j.jenr.20150401.12

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Hsien-Tsung Lee. A Comparison between Statistical Analysis and Grey Model Analysis on Assessed Parameters of Petroleum Potential from Organic Materials. J. Energy Nat. Resour. 2015, 4(1), 5-26. doi: 10.11648/j.jenr.20150401.12

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AMA Style

Hsien-Tsung Lee. A Comparison between Statistical Analysis and Grey Model Analysis on Assessed Parameters of Petroleum Potential from Organic Materials. J Energy Nat Resour. 2015;4(1):5-26. doi: 10.11648/j.jenr.20150401.12

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@article{10.11648/j.jenr.20150401.12,
  author = {Hsien-Tsung Lee},
  title = {A Comparison between Statistical Analysis and Grey Model Analysis on Assessed Parameters of Petroleum Potential from Organic Materials},
  journal = {Journal of Energy and Natural Resources},
  volume = {4},
  number = {1},
  pages = {5-26},
  doi = {10.11648/j.jenr.20150401.12},
  url = {https://doi.org/10.11648/j.jenr.20150401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jenr.20150401.12},
  abstract = {This study aims to investigate the impact of changes in the Tmax  and Ro% on the assessed parameters (S1, S2, S1+S2, HI, QI, BI, PI, TOC) of petroleum potential of organic materials. The samples studied include coals and coaly shales of Mushan Formation, Shihti Formation  and Nanchuang Formation in NW Taiwan, coals and an oil shale from Mainland China, the well-drilled chip samples from NW Australia, in addition to the data of samples were included from literatures. This work will get on the detecting data of 10 parameters (S1, S2, S1+S2, TOC, HI, QI, BI, PI, Ro%, Tmax) and progressing statistical analysis, and focus the study on comparison between grey forecast of grey relational grade and regression model forecast. The results from statistical analysis (include temperature-treated samples were individually subjected to Rock-Eval analysis) of the all parameters data for all samples in this research project, not only be executed a linear regression, curve regression between any two parameters, and multivariate regression, but also be carried on the forecast of grey correlation grade of grey theory (include grey relational generating (Nominal-the-better-:Ro%; Larger-the-better-: Tmax, HI, QI, BI, S2, S1+ S2, S1; smaller-the-better-: TOC, PI) and globalization grey relational grade). So far, obtain roughly the consistency of results from two type predictive analysis. The constructed HI, QI and BI bands were broad at low maturities and gradually narrowed with increasing thermal maturity. The petroleum generation potential is completely exhausted at a vitrinite reflectance of 2.0-2.2% or a Tmax of 510-520°C. An increase in HI and QI suggests extra petroleum potential related to changes in the structure of the organic material. A decline in BI signifies the start of the oil expulsion window and occurs within the vitrinite reflectance range 0.75-1.05 % or a Tmax of 440-455 oC. Furthermore, petroleum potential can be divided into four different parts based on the cross-plot of HI vs. %Ro. The area with the highest petroleum potential is located in sectionⅡ with %Ro=0.6-1.0%, and HI>100. Oil generation potential is rapidly exhausted at section Ⅲ with %Ro >1.0%. This result is in accordance with the regression curve of HI and QI with %Ro based on 97 samples with %Ro=1.0~5.6%.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - A Comparison between Statistical Analysis and Grey Model Analysis on Assessed Parameters of Petroleum Potential from Organic Materials
AU  - Hsien-Tsung Lee
Y1  - 2015/01/30
PY  - 2015
N1  - https://doi.org/10.11648/j.jenr.20150401.12
DO  - 10.11648/j.jenr.20150401.12
T2  - Journal of Energy and Natural Resources
JF  - Journal of Energy and Natural Resources
JO  - Journal of Energy and Natural Resources
SP  - 5
EP  - 26
PB  - Science Publishing Group
SN  - 2330-7404
UR  - https://doi.org/10.11648/j.jenr.20150401.12
AB  - This study aims to investigate the impact of changes in the Tmax  and Ro% on the assessed parameters (S1, S2, S1+S2, HI, QI, BI, PI, TOC) of petroleum potential of organic materials. The samples studied include coals and coaly shales of Mushan Formation, Shihti Formation  and Nanchuang Formation in NW Taiwan, coals and an oil shale from Mainland China, the well-drilled chip samples from NW Australia, in addition to the data of samples were included from literatures. This work will get on the detecting data of 10 parameters (S1, S2, S1+S2, TOC, HI, QI, BI, PI, Ro%, Tmax) and progressing statistical analysis, and focus the study on comparison between grey forecast of grey relational grade and regression model forecast. The results from statistical analysis (include temperature-treated samples were individually subjected to Rock-Eval analysis) of the all parameters data for all samples in this research project, not only be executed a linear regression, curve regression between any two parameters, and multivariate regression, but also be carried on the forecast of grey correlation grade of grey theory (include grey relational generating (Nominal-the-better-:Ro%; Larger-the-better-: Tmax, HI, QI, BI, S2, S1+ S2, S1; smaller-the-better-: TOC, PI) and globalization grey relational grade). So far, obtain roughly the consistency of results from two type predictive analysis. The constructed HI, QI and BI bands were broad at low maturities and gradually narrowed with increasing thermal maturity. The petroleum generation potential is completely exhausted at a vitrinite reflectance of 2.0-2.2% or a Tmax of 510-520°C. An increase in HI and QI suggests extra petroleum potential related to changes in the structure of the organic material. A decline in BI signifies the start of the oil expulsion window and occurs within the vitrinite reflectance range 0.75-1.05 % or a Tmax of 440-455 oC. Furthermore, petroleum potential can be divided into four different parts based on the cross-plot of HI vs. %Ro. The area with the highest petroleum potential is located in sectionⅡ with %Ro=0.6-1.0%, and HI>100. Oil generation potential is rapidly exhausted at section Ⅲ with %Ro >1.0%. This result is in accordance with the regression curve of HI and QI with %Ro based on 97 samples with %Ro=1.0~5.6%.
VL  - 4
IS  - 1
ER  -

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