期刊文献+

A Mathematical Calculation Model Using Biomarkers to Quantitatively Determine the Relative Source Proportion of Mixed Oils 被引量:3

A Mathematical Calculation Model Using Biomarkers to Quantitatively Determine the Relative Source Proportion of Mixed Oils
下载PDF
导出
摘要 It is difficult to identify the source(s) of mixed oils from multiple source rocks, and in particular the relative contribution of each source rock. Artificial mixing experiments using typical crude oils and ratios of different biomarkers show that the relative contribution changes are non-linear when two oils with different concentrations of biomarkers mix with each other. This may result in an incorrect conclusion if ratios of biomarkers and a simple binary linear equation are used to calculate the contribution proportion of each end-member to the mixed oil. The changes of biomarker ratios with the mixing proportion of end-member oils in the trinal mixing model are more complex than in the binary mixing model. When four or more oils mix, the contribution proportion of each end-member oil to the mixed oil cannot be calculated using biomarker ratios and a simple formula. Artificial mixing experiments on typical oils reveal that the absolute concentrations of biomarkers in the mixed oil cause a linear change with mixing proportion of each end-member. Mathematical inferences verify such linear changes. Some of the mathematical calculation methods using the absolute concentrations or ratios of biomarkers to quantitatively determine the proportion of each end-member in the mixed oils are deduced from the results of artificial experiments and by theoretical inference. Ratio of two biomarker compounds changes as a hyperbola with the mixing proportion in the binary mixing model, as a hyperboloid in the trinal mixing model, and as a hypersurface when mixing more than three end- members. The mixing proportion of each end-member can be quantitatively determined with these mathematical models, using the absolute concentrations and the ratios of biomarkers. The mathematical calculation model is more economical, convenient, accurate and reliable than conventional artificial mixing methods. It is difficult to identify the source(s) of mixed oils from multiple source rocks, and in particular the relative contribution of each source rock. Artificial mixing experiments using typical crude oils and ratios of different biomarkers show that the relative contribution changes are non-linear when two oils with different concentrations of biomarkers mix with each other. This may result in an incorrect conclusion if ratios of biomarkers and a simple binary linear equation are used to calculate the contribution proportion of each end-member to the mixed oil. The changes of biomarker ratios with the mixing proportion of end-member oils in the trinal mixing model are more complex than in the binary mixing model. When four or more oils mix, the contribution proportion of each end-member oil to the mixed oil cannot be calculated using biomarker ratios and a simple formula. Artificial mixing experiments on typical oils reveal that the absolute concentrations of biomarkers in the mixed oil cause a linear change with mixing proportion of each end-member. Mathematical inferences verify such linear changes. Some of the mathematical calculation methods using the absolute concentrations or ratios of biomarkers to quantitatively determine the proportion of each end-member in the mixed oils are deduced from the results of artificial experiments and by theoretical inference. Ratio of two biomarker compounds changes as a hyperbola with the mixing proportion in the binary mixing model, as a hyperboloid in the trinal mixing model, and as a hypersurface when mixing more than three end- members. The mixing proportion of each end-member can be quantitatively determined with these mathematical models, using the absolute concentrations and the ratios of biomarkers. The mathematical calculation model is more economical, convenient, accurate and reliable than conventional artificial mixing methods.
出处 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 2007年第5期817-826,共10页 地质学报(英文版)
关键词 mixed oil BIOMARKER oil source correlation quantitative determination mathematical model mixed oil, biomarker, oil source correlation, quantitative determination, mathematical model
  • 相关文献

参考文献5

二级参考文献22

  • 1王屿涛.从澳大利亚煤成烃条件探讨准噶尔盆地煤成烃问题[J].新疆石油地质,1994,15(2):136-141. 被引量:9
  • 2Peters K E, Clutson M J, Roberson G. Mixed marine and lacustrine input to an oil-cemented sandstone breccia from Brora[J]. Scotland. Organic Geochemistry, 1999,30: 237-248.
  • 3Dzou L I, Holba A G, Ramon J G, et al. Application of new diterpane biomarker to source,biodegradation and mixing effects on Central Llanos Basin oils, Colombia [J]. Organic Geochemistry, 1999,30 : 515-534.
  • 4Peters K E,Moldwan J M,Driscole A R,et al. Origin of beatrice oil by co-sourcing from Devonian and Middle Jurassic source rocks[J]. Inner Moray Firth,U. K. AAPG Bull. ,1989,73:454-471.
  • 5Chen J P,Deng C P,Liang D G,et al. Mixed oils derived from multiple source rocks in the Cainan Oilfield, Junggar Basin,Northwest China. Part II: Artificial mixing experiments on typical crude oils and quantitative oil-source correlation [J].Organic Geoche
  • 6Peters K E, Moldowan J M. The biomaker guide: interpreting molecular fossils in petroleum and ancient sediments[M]. New Jersey: Prentice-Hall, Inc. , 1993. 252-265.
  • 7黄第藩.克拉玛依油田形成中石油运移的地球化学[J].中国科学,B辑,1988,12:1308-1315.
  • 8王培荣 何文祥 席小应.珠三坳陷油源及油气二次运移方法研究[R].江汉石油学院地球化学研究中心,海洋石油勘探开发研究,1999..
  • 9周毅 张通彩 卢景美.渤海海域凸起及周缘油气资源评价[R].海洋石油勘探开发研究中心,1999..
  • 10王培荣 宋孚庆 徐冠军.渤中坳陷油源指标的建立和混源油研究[R].北京:中国石油勘探开发研究院实验中心,荆州:CNPCKLPG,2003..

共引文献136

同被引文献40

引证文献3

二级引证文献23

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部