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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Akande SO, Ojo OJ, Erdtmann BD, Hetenyi M (1998) Paleoenvironments, source rock potential and thermal maturity of the Upper Benue rift basins, Nigeria : implications for hydrocarbon exploration. Organic Geochemistry29 (1-3): 531-542
[2]	Amijaya H, Littke R (2006) Properties of thermally metamorphosed coal from Tanjung Enim Area, South Sumatra Basin, Indonesia with special reference to the coalification path of macerals. International Journal of Coal Geology 66:271-295
[3]	Arfaoui A, Montacer M, Kamoun F, Rigane A (2007) Comparative study between Rock-Eval pyrolysis and biomarkers parameters: A case study of Ypresian source rocks in central-northern Tunisia. Marine and Petroleum Geology 24:566-578
[4]	ASTM (1975) Standard D-2797, ASTM Standard manual. Part 26:350-354
[5]	ASTM (1980) Standard D-2797, Microscopical determination of volume percent of physical components in a polished specimen of coal, ASTM. Philadelphia, Pa.
[6]	Banerjee A, Sinha AK, Jain AK, Thomas NJ, Misra KN, Chandra, K (1998) A mathematical representation of Rock-Eval hydrogen index vs. Tmax profiles. Organic Geochemistry28 (no.1/2):43-55
[7]	Bordenave ML, Espitalie’ J, Leplat P, Oudin JL, Vandenbroucke M (1993) Screening techniques for source rock evaluation, In: Bordenave, M.L. (Ed.), Applied Petroleum Geochemistry. Editions Technip, Paris, pp 219–224
[8]	BostickNH, Daws TA (1994) Relationships between data from Rock-Eval pyrolysis and proximate, ultimate, petrographic, and physical analyses of 142 diverse U. S. coal samples. Organic Geochemistry21:35–49
[9]	Canonico U, Tocco R, Ruggiero A, Suarez H (2004) Organic geochemistry and petrology of coals and carbonaceous shales from western Venezuela. International Journal of Coal Geology 57:151-165
[10]	Chen J, Liang D, Wang X, Zhong N, Song F, Deng C, Shi X, Jin T, Xiang S (2003)Mixed oils derived from multiple source rocks in the Cainan oilfield, Junggar Basin, Northwest China. PartⅠ: genetic potential of source rocks, features of biomarkers and oil sources of typical crude oil. Organic Geochemistry 34:889-909
[11]	Chen J-P, Deng C-P, Wang H-T, Han D-X (2006) Genetic potential and geochemical features of pyrolysis oils of macerals from Jurassic coal measures, Northwest China. Geochimica 1:81-87
[12]	Chiu J-H, Kuo C-L, Lin H-J, Chou T-H (1993) Geochemical modeling of source rock hydrocarbon generation potential. Exploration Development Research Report 16: 232-256
[13]	Chiu J-H, Kuo C-L, Wu S-H, Lin L-H, Shen J-C, Chou T-H (1996) Simulation of the hydrocarbon generation potential of the coal samples. Exploration Development Research Report 19:420-453
[14]	Cooles GP, Mackenzie AS, Quigley JM (1986) Calculation of petroleum masses generated and expelled from source rocks. Organic Geochemistry 10:235-245
[15]	Dahl B, Bojesen-Koefoed J, Holm A, Justwan H, Rasmussen E, Thomsen E (2004) A new approach to interpreting Rock-Eval S2 and TOC data for kerogen quality assessment. Organic Geochemistry35:1461-1477
[16]	Davis RC, Noon SW, Harrington J (2007) The petroleum potential of Tertiary coals from Western Indonesia: Relationship to mire type and sequence stratigraphic setting. International Journal of Coal Geology 70:35-52
[17]	Deng J-L (1988) Essential topics on grey system: theory and application, China Ocean Press, pp 327
[18]	Espitalie’J, Deroo G, Marquis F (1985) La pyrolyse Rock-Eval et ses applications. Revue Institut Franc- ais du Pe’ trole Part I40:563–578, Part II40:755–784
[19]	Espitalie’ J, Laporte JL, Madec M, Marquis F, Leplat P, Paulet J, Boutefeu F (1977) Me’ thode rapide de caracte’ risation des roches me` res, de leur potentiel pe’ trolier et de leur degre’ d’e’ volution. Revue Institut Franc- ais du Pe’ trole 32:23–42
[20]	Hu C-J (2001) The application of Rock-Eval 6 in geochemical exploration. Exploration Development Research Report 23:367-378
[21]	Hunt JM (1996) Petroleum Geochemistry and Geology. W.H. Freeman and Company, New York, pp 743
[22]	ISO 7404-5 (1994E)Methods for the petrographic analysis of bituminous coal and anthracite Part 5 Method of determining microscopically the reflectance of vitrinite, International Standard, 2nd ed., pp 1-13
[23]	Karakitsios V, Rigakis N (2007) Evolution and petroleum potential of Western Greece, Journal of Petroleum Geology 30(3):197-218
[24]	Katz BJ (1983) Limitation of ‘Rock-Eval’ pyrolysis for typing organic matter, Organic Geochemistry 4:195-199
[25]	Keller G (2001) Applied statistics with Microsoft Excel, Thomson Learning Asia Pte Ltd, Beijing, pp 670
[26]	Killops SD, Funnell RH, Suggate RP, Sykes R, Peters KE, Walters C, Woolhouse AD, Weston RJ(1998) Predicting generation and expulsion of paraffinic oil from vitrinite-rich coals. Org. Geochem 29(1-3):1-21
[27]	Killops SD, Funnell RH, Suggate RP, Sykes R, Peters KE, Walters C, Woolhouse AD, Weston RJ, Boudou J-P (1998) Predicting generation and expulsion of paraffinic oil from vitrinite-rich coals. Organic Geochemistry 29:1–21
[28]	Kotarba MJ, ClaytonJL, Rice DD, Wagner M (2002) Assessment of hydrocarbon source rock potential of Polish bituminous coals and carbonaceous shales, Chemical Geology184:11-35
[29]	Kotarba MJ, Wiectaw D, Koltun YV, Marynowski L, Kusmierek J, Dudok IV (2007) Organic geochemistry study and genetic correlation of natural gas, oil and Menilite source rocks in the area between San and Stryi rivers (Polish and Ukrainian Carpathians). Organic Geochemistry 38(8): 1431-1456
[30]	Kotarba M, Lewan MD (2004) Characterizing thermogenic coalbed gas from Polish coals of different ranks by hydrous pyrolysis. Organic Geochemistry35:615-646
[31]	Lee H-T (2011) Analysis and characterization of samples from sedimentary strata with correlations to indicate the potential for hydrocarbons, Environmental Earth Sciences 64:1713-1728
[32]	Lee, H-T, Sun, L-C (2013) The atomic H/C ratio of kerogen and its relation to organic geochemical parameters : implications for evaluating hydrocarbon generation of source rock, Carbonates and Evaporites 28(4):433-445
[33]	Liu D-H, Zhang H-Z, Dai J-X, Sheng G-Y, Xiao X-M, Sun Y-H, Shen J-G (2000) The research and evaluation of forming hydrocarbon from micro-constituent of coal rock. Chinese Science Bull.45(4):346-352
[34]	Magoon LB, Dow WG (1994) The petroleum system-from source to trap. AAPG Memoir 60, Tulsa, Oklahoma, U.S.A., pp 655
[35]	Newman J, Boreham CJ, Ward SD, Murray AP, Bal AA (1999) Floral influences on the petroleum source potential of New Zealand coals. In: Masterlerz, M., Glikson, M., Golding, S.D. (Eds.), Coalbed Methane: Scientific, Environmental and Economic Evaluation. Kluwer Academic pp 461–492
[36]	Norgate CM, Boreham CJ, WilkinsAJ (1999) Changes in hydrocarbon maturity indices with coal rank and type, Buller Coalfield, New Zealand. Organic Geochemistry30:985-1010
[37]	Pedersen GK, Andersen LA, Lundsteen EB, Petersen HI, Bojesen-Koefoed JA, Nytoft HP (2006) Depositional environments, organic maturity and petroleum potential of the Cretaceous Coal-Bearing Atane Formation at Qullissat, Nuussuaq Basin, West Greenland. Journal of Petroleum Geology 29(1):3-26
[38]	Pepper AS, Corvi PJ (1995) Simple kinetic models of petroleum formation. Part I: oil and gas generation from kerogen. Marine Petroleum Geology 12 (3):291–319
[39]	Peters KE, Cassa MR (1994) Applied source-rock geochemistry, In: Magoon, L.B., Dow, W.G. (Eds.), The Petroleum System—From Source to Trap. American Association of Petroleum Geologists, Memoir 60:93–120
[40]	Peters KE (1986) Guidelines for evaluating petroleum source rocks using programmed pyrolysis. American Association of Petroleum Geologists Bulletin 70:318–329
[41]	Petersen HI (2002) A reconsideration of the “oil window” for humic coal and kerogen type Ⅲ source rocks. Journal of Petroleum Geology 25(4):407-432
[42]	Petersen HI (2006) The petroleum generation potential and effective oil window of humic coals related to coal composition and age. International Journal of Coal Geology 67:221-248
[43]	Petersen HI, Nytoft HP, Nielsen LH (2004) Characterisation of oil and potential source rocks in the northeastern Song Hong Basin, Vietnam: indications of a lacustrine-coal sourced petroleum system. Organic Geochemistry 35:493-515
[44]	Petersen HI, Rosenberg P, Andsbjerg J (1996) Organic geochemistry in relation to the depositional environments of Middle Jurassic coal seams, Danish Central Graben, and implications for hydrocarbon generative potential. AAPG Bulletin 80(1):47-62
[45]	Petersen HI, Tru V, Nielsen LH, Due NA, Nytoft HP (2005) Source rock properties of lacustrine mudstones and coals (Oligocene Dong Ho formation), onshore Song Hong Basin, Northern Vietnam. Journal of Petroleum Geology28(1):19-38
[46]	Powell TG, Boreham CJ, Smyth M, Russell N, Cook AC (1991) Petroleum source rock assessment in non-marine sequences : pyrolysis and petrographic analysis of Australian coals and carbonaceous shales. Organic Geochemistry17(3):375-394
[47]	RabbaniAR, Kamali MR (2005) Source rock evaluation and petroleum geochemistry offshore SW Iran. Journal of Petroleum Geology 28(4):413-428
[48]	Sachsenhofer RF, Privalov VA, Izart A, Elie M, Kortensky J, Panova E A, Sotirov A, Zhykalyak MV (2003) Petrography and geochemistry of Carboniferous coal seamsin the Donets Basin (Ukraine): implications for paleoecology. International Journal of Coal Geology55:225-259
[49]	Suggate RP, Boudou JP (1993) Coal rank and type variation in Rock-Eval assessment of New Zealand coals, J. Pet. Geol. 16:73–88
[50]	Sun X-G, Qin S-F, Luo J, Jin K-L (2001) A study of activation energy of coal macerals. Geochimica 30(6):559-604
[51]	Sykes R (2001) Depositional and rank controls on the petroleum potential of coaly source rocks. In: Hill, K.C., Bernecker, T. (Eds.), Eastern Australasian Basins Symposium, a Refocused Energy Perspective for the Future. Petrol. Expl. Soc. Austral., Spec. Publ.pp 591–601
[52]	Sykes R, Snowdon LR (2002) Guidelines for assessing the petroleum potential of coaly source rocks using Rock-Eval pyrolysis. Org. Geochem. 33:1441–1455
[53]	Taylor GH, Teichmüller M, Davis A, Diessel CFK, Littke R, Robert P (1998) Organic Petrology. Gebrüder Borntraeger, Berlin, Stuttgart, pp 704
[54]	Teichmüller M, Durand B (1983) Fluorescence microscopical rank studies on liptinites and vitrinites in peat and coals, and comparison with results of the Rock-Eval pyrolysis. Int. J. Coal Geol. 2:197–230
[55]	Tissot BP, Welte DH (1984) Petroleum formation and occurrence ; a New approach to oil gas exploration. Springer-Verlag, Berlin, Heidelberg, New York, pp 699
[56]	Tissot BP, Pelet R, Ungerer P (1987) Thermal history of sedimentary basins, maturation indices, and kinetics of oil and gas generation. AAPG Bull. 71:1445–1466
[57]	Vassoevich NB, Akramkhodzhaev AM, Geodekyan AA (1974) Principal zone of oil formation. In: Tissot, B., Bienner, F. (Eds.), Advances in Organic Geochemistry 1973, Éditions Technip, Paris,pp309–314
[58]	Veld H, Fermont WJJ, Jegers LF (1993) Organic petrological characterization of Westphalian coals from The Netherlands : correlation between Tmax, vitrinite reflectance and hydrogen index. Organic Geochemistry20(6):659-675
[59]	Wang C-J (1998) A “folded-fan” method for assessment on the hydrocarbon generating potential of coals. Geochimica 27(5):4 83-492
[60]	Wen K-L (2004) Grey Systems Modeling and Prediction, Yang’s Scientific Research Institute, USA, pp 468
[61]	Wu S-H, Weng R-N, Shen J-Q, Sun Z-X, Guo Z-L (2003) The study for constituent characteristics of the hydrocarbon compound of coal and coal shales in the NW Taiwan, Exploration Development Research Report 25:229-239
[62]	Xiao XM, Hu ZL, Jin YB, Song ZG (2005) Hydrocarbon source rocks and generation history in The Lunnan Oilfield area, northern Tarim Basin (NW China). Journal of Petroleum Geology28(3):319-333
[63]	Xiao X (1997) The organic petrological characteristics of Triassic source rocks and their hydrocarbon-generating potential in Tarim Basin. Geochimica 26(1):64-71
[64]	Xiao X, Liu D, Fu J (1996) The evaluation of coal-measure source rocks of coal-bearing basins in China and their hydrocarbon-generating models. Acta Sedimentologica Sincia 14(supp.):10-17
[65]	Zhang T-P, Zhang Y-C, Cai K-Z (2007) SPSS Statistic modeling and analytic procedure. Kings Information Co., Ltd., Taipei, pp 674

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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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