基于高斯小波变换的测井曲线自动分层模型

基于高斯小波变换的测井曲线自动分层模型Title: An Automatic Layering Model for Well Logging Curves based on the Gaussian

基于高斯小波变换的测井曲线自动分层模型 Title:AnAutomaticLayeringModelforWellLoggingCurvesbasedonthe GaussianWaveletTransform Abstract: Theinterpretationofwellloggingcurvesplaysacrucialroleintheoilandgas industry,providingvaluableinformationforreservoircharacterization. However,manualinterpretationisatime-consumingandsubjectiveprocess, leadingtopotentialerrorsandinconsistencies.Inthispaper,weproposean automaticlayeringmodelforwellloggingcurvesbasedontheGaussian wavelettransform.Themodelaimstoimprovetheaccuracyandefficiencyof theinterpretationprocess,ultimatelyenhancingreservoircharacterizationand decision-making. 1.Introduction Wellloggingisanessentialtechniqueusedinthepetroleumindustryto obtaindetailedinformationaboutsubsurfaceformations.Theselogging curves,representingtheresponseofdifferentmeasurementstothegeologic formation,providevaluableinsightsintolithology,porosity,fluidsaturation, andotherproperties.However,themanualinterpretationofwelllogging curvesissubjective,time-consuming,andpronetohumanerrors.Therefore, theneedforautomatedalgorithmstoaccuratelyinterpretwellloggingcurves hasbecomeincreasinglyimportant. 2.GaussianWaveletTransform TheGaussianwavelettransformisapowerfulsignalprocessingtechniquethat decomposesasignalintoitsfrequencycomponents.Ithasbeensuccessfully appliedinvariousfields,includingimageprocessingandpatternrecognition. ByapplyingtheGaussianwavelettransformtowellloggingcurves,wecan identifyvariousfrequencycomponentsrelatedtodifferentformationsor layers. 3.Methodology Theproposedautomaticlayeringmodelconsistsofthefollowingsteps: 3.1Preprocessing Initially,thewellloggingcurvesarepreprocessedtoremovenoiseand inconsistenciescausedbyloggingtoolsandenvironmentalfactors.Common preprocessingtechniquessuchasmedianfilteringandnormalizationare employedtoenhancethequalityofthedata. 3.2GaussianWaveletTransform Thepreprocessedwellloggingcurvesarethendecomposedusingthe Gaussianwavelettransform.Thistransformbreaksdownthesignalinto differentfrequencycomponents,revealinghiddenpatternsandfeaturesinthe