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基于高斯小波变换的测井曲线自动分层模型.docx

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基于高斯小波变换的测井曲线自动分层模型 Title:AnAutomaticLayeringModelforWellLoggingCurvesbasedontheGaussianWaveletTransform Abstract: Theinterpretationofwellloggingcurvesplaysacrucialroleintheoilandgasindustry,providingvaluableinformationforreservoircharacterization.However,manualinterpretationisatime-consumingandsubjectiveprocess,leadingtopotentialerrorsandinconsistencies.Inthispaper,weproposeanautomaticlayeringmodelforwellloggingcurvesbasedontheGaussianwavelettransform.Themodelaimstoimprovetheaccuracyandefficiencyoftheinterpretationprocess,ultimatelyenhancingreservoircharacterizationanddecision-making. 1.Introduction Wellloggingisanessentialtechniqueusedinthepetroleumindustrytoobtaindetailedinformationaboutsubsurfaceformations.Theseloggingcurves,representingtheresponseofdifferentmeasurementstothegeologicformation,providevaluableinsightsintolithology,porosity,fluidsaturation,andotherproperties.However,themanualinterpretationofwellloggingcurvesissubjective,time-consuming,andpronetohumanerrors.Therefore,theneedforautomatedalgorithmstoaccuratelyinterpretwellloggingcurveshasbecomeincreasinglyimportant. 2.GaussianWaveletTransform TheGaussianwavelettransformisapowerfulsignalprocessingtechniquethatdecomposesasignalintoitsfrequencycomponents.Ithasbeensuccessfullyappliedinvariousfields,includingimageprocessingandpatternrecognition.ByapplyingtheGaussianwavelettransformtowellloggingcurves,wecanidentifyvariousfrequencycomponentsrelatedtodifferentformationsorlayers. 3.Methodology Theproposedautomaticlayeringmodelconsistsofthefollowingsteps: 3.1Preprocessing Initially,thewellloggingcurvesarepreprocessedtoremovenoiseandinconsistenciescausedbyloggingtoolsandenvironmentalfactors.Commonpreprocessingtechniquessuchasmedianfilteringandnormalizationareemployedtoenhancethequalityofthedata. 3.2GaussianWaveletTransform ThepreprocessedwellloggingcurvesarethendecomposedusingtheGaussianwavelettransform.Thistransformbreaksdownthesignalintodifferentfrequencycomponents,revealinghiddenpatternsandfeaturesinthecurves