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一种新的toc含量拟合方法研究与应用

Keywords: 测井响应模型,bp神经网络,模糊排队,toc含量,鄂尔多斯盆地

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Abstract:

?由于取样的限制,通常在烃源岩评价中往往只能获得十分有限的实测烃源岩的toc含量数据。但随着我国多数盆地精细勘探的深入,需要更加精细的烃源岩评价,因此,人们对测井数据拟合烃源岩的toc含量变化越来越重视。目前该技术最先进的是人工神经网络拟合方法,但多种神经网络在拟合高成熟度碳酸盐岩烃源岩地层的toc含量时相关性均不够高。针对研究区内烃源岩的特点,尝试了一种用图版分类—模糊排队—bp神经网络联合拟合toc含量的新方法。利用该方法对鄂尔多斯盆地马家沟组的测井资料和实测总有机碳资料进行数据处理,将结果与δlogr方法、模糊神经网络方法的结果相比较,证实该方法的结果与实测值有更好的相关性,为系统了解鄂尔多斯盆地下古生界烃源岩发育提供了一定的科学依据。

References

[1]  mendelsonj,toksozmn.sourcerockcharacterizationusingmultivariateanalysisoflogdata,in:trans[r].spwlaann.loggingsymp.,1985,26:uu1-uu21.
[2]  davisjc.statisticanddataanalysisingeology,2ndedition[m].newyork:johnwiley,1986:646.
[3]  passeyqr,creaneys,kullajb,etal.apracticalmodelfororganicrichnessfromporosityandresistivitylogs[j].aapgbulletin,1990,74(12):1777-1794.
[4]  连承波,李汉林,渠芳,等.基于测井资料的b神经网络模型在孔隙度定量预测中的应用[j].天然气地球科学,2006,17(3):382-384.
[5]  朱创业,张寿庭.鄂尔多斯盆地马家沟组碳酸盐岩有机质特征及烃源岩研究[j].成都理工学院学报,1999,26(3):217-220.
[6]  王飞宇,何萍,程顶胜,等.下古生界高—过成熟烃源岩有机成熟度评价[j].天然气地球科学,1994,5(6):1-14.
[7]  beersrf.radioactivityandorganiccontentofsomepaleozoicshales[j].aapgbulletin,1945,29:1-22.
[8]  swansonve.oilyieldanduraniumcontentofblackshales[r].usgsprofessionalpaper356-a,1960:1-44.
[9]  tixiermp,curtismr.oilshaleyieldpredictedfromwelllogs,in:drillingandproduction[c].7thworldpetroleumcongregation,mexicocity,1967,elsevier.
[10]  schmokerjw.determinationoforganiccontentofappalachiandevonianshalesfromformation-densitylogs[j].aapgbulletin,1979,63:1504-1537.
[11]  schmokerjw.determinationoforganic-mattercontentofappalachiandevonianshalefromgamma-raylogs[j].aapgbulletin,1981,65:1285-1298.
[12]  schmokerjw,hestertc.organiccarboninbakkenformation,unitedstatesportionofeillistonbasin[j].aapgbulletin,1983,67:2165-2174.
[13]  herronsl.atotalorganiccarbonlogforsourcerockevaluation[j].theloganalyst,1987,28(6):520-527.
[14]  herronsl,letendrel.wirelinesource-rockevaluationintheparisbasin[m]//hucay.depositionoforganicfacies.aapgstudiesingeology,1990,30:57-71.
[15]  dellenbachj,espitaliej,lebretonf.sourcerockloggingtrans[r].8theuropeanspwlasymp.,1983,paperd.
[16]  meyerbl,nederlofmh.identificationofsourcerocksonwirelinelogsbydensity/resistivityandsonictransittime/resistivitycross-plots[j].aapgbulletin,1984,68:121-129.
[17]  huangzehui,williamsonma.artificialneuralnetworkmodelingasanaidtosourcerockcharacterization[j].marineandpetroleumgeology,1996,13(2):277-290.
[18]  rumelhartde,mclellandjl.paralleldistributedprocessing:explorationinthemicrostructureofcognition[m].cambridge,ma:mitpress,1986:3-44.
[19]  王贵文,朱振宇,朱广宇.烃源岩测井识别与评价方法研究[j].石油勘探与开发,2002,29(4):50-52.
[20]  mohammadrezakamalia,ahadallahmirshady.totalorganiccarboncontentdeterminedfromwelllogsusingδlograndneurofuzzytechniques[j].journalofpetroleumscienceandengineering,2004,45:141-148.
[21]  张水昌,梁狄刚,张大江.关于古生界烃源岩有机质丰度的评价标准[j].石油勘探与开发,2002,29(2):8-12.
[22]  fanre,changkw,hsiehcj,etal.liblinear:alibraryforlargelinearclassification[j].journalofmachinelearningresearch,2008,9:1871-1874.
[23]  limjs.reservoirpropertiesdeterminationusingfuzzylogicandneuralnetworks[j].journalofpetroleumscienceandengineering,2005,49:182-192.
[24]  李贤庆,侯读杰,胡国艺,等.鄂尔多斯盆地中部地区下古生界碳酸盐岩生烃潜力探讨[j].矿物岩石地球化学通报,2002,21(3):153-157.

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