一种估算原油成熟度的新方法-豆柴文库

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一种估算原油成熟度的新方法
Title:ANovelApproachtoEstimatingCrudeOilMaturity
Abstract:
Crudeoilmaturityisacriticalparameterinthepetroleumindustry,asitprovidesvitalinformationaboutthecompositionandpropertiesofhydrocarbonreservoirs.Traditionalmethodsforestimatingcrudeoilmaturity,suchasvitrinitereflectance,Tmax,andbiomarkeranalysis,possesslimitationsinaccuracyandaccessibility.Thispaperexploresanewapproachtoestimatingcrudeoilmaturity,utilizingadvancedmachinelearningtechniquesandacomprehensivedataset.Theproposedmethoddemonstratespromisingresultsandhasthepotentialtorevolutionizehowcrudeoilmaturityisestimated.
Introduction:
Crudeoilmaturityreferstothelevelofthermalalterationandtransformationthathydrocarbonsourcerocksandtheirderivedproductshaveundergone.Itprovidescrucialinsightsintothehydrocarbongeneration,expulsion,andmigrationprocessesinreservoirs.Accurateestimatesofcrudeoilmaturityareessentialforreservoirevaluation,explorationandproductionplanning,andeconomicallyviabledecision-makinginthepetroleumindustry.
Currentmethodsusedtoestimatecrudeoilmaturity,suchasvitrinitereflectance,Tmax,andbiomarkeranalysis,facechallengesintermsofaccuracy,time-consumingprocedures,andthesuitabilityforvarioustypesofcrudeoil.Theselimitationshighlighttheneedforanovelapproachtoestimatingcrudeoilmaturity,whichcanovercometheseshortcomingsandprovidereliableresults.
Methods:
Theproposedmethodemploysadvancedmachinelearningtechniquesandacomprehensivedatasettoestimatecrudeoilmaturityaccurately.Thedatasetconsistsofadiverserangeofcrudeoilsamplescollectedfromdifferentreservoirsworldwide,coveringvariousgeologicenvironmentsandthermalalterationlevels.
Themachinelearningmodelistrainedusingthisdataset,incorporatingacombinationofsupervisedandunsupervisedlearningalgorithms.Thesupervisedlearningsectionutilizeslabeleddatatopredictcrudeoilmaturitybasedonvariousinputparameters,suchasAPIgravity,geochemicalcompositions,andthermalalterationindicators.Theunsupervisedlearningsectionutilizesunlabeleddatatoidentifypatternsandsimilaritiesw