一種基于深度神經網絡模型的測井曲線生成方法

一種基于深度神經網絡模型的測井曲線生成方法摘要測井曲線是油田勘探和開發(fā)中常用的一種工具，在地層分析和油氣儲量預測中具有重要作用。本論文提出了一種基于深度神經網絡模型的測井曲線生成方法。該方法通過對測井數據進行處理和轉化，得到數據集，并構建了多層感知機（MLP）神經網絡模型，訓練模型并對其進行了驗證。實驗結果表明，該方法能夠生成具有較高精度和準確性的測井曲線，并具有較強的實用性和廣泛適用性。關鍵詞：測井曲線；深度神經網絡；多層感知機；數據處理AbstractWellloggingcurveisacommonlyusedtoolinoilfieldexplorationanddevelopment,whichplaysanimportantroleingeologicalanalysisandoilandgasreservesprediction.Inthispaper,weproposeamethodofwellloggingcurvegenerationbasedondeepneuralnetworkmodel.Byprocessingandtransformingthewellloggingdata,thedatasetisobtained,andthemulti-layerperceptron(MLP)neuralnetworkmodelisconstructed,andthemodelistrainedandverified.Theexperimentalresultsshowthattheproposedmethodcangeneratewellloggingcurveswithhighaccuracyandprecisionandhasstrongpracticalityandwideapplicability.Keywords:wellloggingcurve;deepneuralnetwork;multi-layerperceptron;dataprocessing1.IntroductionWellloggingisamethodformeasuringphysicalpropertiesofrocksandsoilsinaborehole.Throughtheanalysisofthevariationsinthedataofthewellloggingcurves,basicphysicalparametersoftherocks,suchasdensity,porosity,andsaturation,canbeobtained.Thesephysicalparametersprovidethebasisforreservoirevaluation,fluididentification,andformationevaluation.However,theaccuracyofwellloggingdataisinfluencedbymanyfactors,suchasboreholeconditions,measurementtools,anddataacquisitionmethods,whichmayresultindatadeviatingfromtheactualvaluesinvaryingdegrees.Toimprovetheaccuracyofwellloggingdataandpromotethedevelopmentofoilandgasexplorationanddevelopment,manymethodshavebeenproposed.Thesemethodsincludestatisticalmethods,physicalmodelingmethods,andmachinelearningmethods[1].Amongthem,machinelearningmethodshavebecomeincreasinglypopularinrecentyears,andhaveshowngreatpotentialinoilfielddataanalysisandprocessing[2].Asawidelyusedmachinelearningmethod,deepneuralnetwork(DNN)hasattractedmuchattentionduetoitspowerfulfeatureextractionandmodelingability[3][4].DNNcanlearncomplexnon-linearrelationshipsamongmassivedataandextractmulti-levelfeaturesfromthedata,whichhasshowngoodperformanceinmanyfields,suchasnaturallanguageprocessing,computervision,andspeechrecognition.Inthispaper,weproposeamethodofwellloggingcurvegenerationbasedondeepneuralnetworkmodel.TheproposedmethodpreprocesseswellloggingdatatoobtainadatasetandconstructsaMLPneuralnetworkmodeltogeneratewellloggingcurves.Theexperimentalresultsshowthattheproposedmethodcangeneratewellloggingcurveswithhighaccuracyandprecision.Therestofthispaperisorganizedasfollows.Section2presentsrelatedworks.Section3describestheproposedmethod.Section4presentstheexperimentalresultsandanalysis.Section5concludesthepaperandsuggestsfuturework.2.RelatedworksInrecentyears,manyresearchershavestudiedwellloggingdataanalysisandmodelingusingmachinelearningmethods.Forexample,Lietal.proposedanewmethodforformationevaluationusingfuzzylogic[5].Zhaoetal.proposedanimprovedsupportvectorregressionmodelforpredictingporosityusingwellloggingdata[6].Zhangetal.usedmachinelearningmethodstoidentifyoilandwatersaturationfromwellloggingdata[7].Allthesemethodshaveachievedgoodperformanceindifferentaspectsofwellloggingdataprocessing.DNNhasalsobeenappliedtowellloggingdataanalysis,andhasshownitsgreatpotentialincharacterizingtheformationpropertiesofrocks[8].Forexample,Xingetal.proposedaconvolutionalneuralnetwork(CNN)modelforwelllogprediction[9].Wangetal.usedaDNNmodeltopredictporositythroughtheintegrationofwelllogging,geological,andpetrophysicaldata[10].Zhouetal.proposedahierarchicalclassificationmethodbasedonDNNandachievedgoodresultsintheidentificationofoilandwater[11].However,comparedwithtraditionalwellloggingdataprocessingmethods,usingDNNforwellloggingcurvegenerationhasbeenlessstudied,andonlyafewmethodshavebeenproposed.Chengetal.proposedanewmethodofwellloggingcurvereconstructionbasedonDNN,whichcaneffectivelyrestoreaccurateloggingcurveinformationfromtheartificiallydamagedloggingcurves[12].Zhangetal.proposedamethodofwellloggingcurvegenerationbasedonrecurrentneuralnetwork,butthegeneratedwellloggingcurvesdidnothavehighaccuracyandprecision[13].Inthispaper,weproposeanewmethodofwellloggingcurvegenerationbasedonMLPneuralnetwork,whichcangeneratewellloggingcurveswithhighaccuracyandprecision.3.Methodology3.1DatapreprocessingTherawwellloggingdataisusuallyverylargeandcomplex,andmaycontainnoise,outliers,andmissingvalues.Togenerateaccuratewellloggingcurves,datapreprocessingisnecessary.Thefirststepofdatapreprocessingistoeliminatenoiseandoutliers.Inthispaper,weusethemovingaveragemethodtosmoothandfilterthedata.Themovingaveragemethodreplaceseachpointofthetimeseriesdatawiththeaverageofitsneighboringpoints.Thesecondstepistodealwithmissingvalues.Missingvaluesmayoccurduetoincompleteorfaultymeasurement,orbecausetheloggingtooldoesnotrecorddataforcertainintervals.Themostcommonmethodfordealingwithmissingvaluesistoperforminterpolation.Weusethelinearinterpolationmethodtofillinmissingvalues.Thelaststepofdatapreprocessingisdatanormalization.Datanormalizationisnecessaryforneuralnetworktrainingtoachievebetterperformance.Weusethemin-maxnormalizationmethodtonormalizethedata,whichmapstheoriginaldatatoarangeof[0,1].3.2MLPneuralnetworkMLPisatypeoffeedforwardneuralnetworkthatconsistsofmultiplelayersofnodes,witheachnodefullyconnectedtothenodesinthepreviousandthenextlayers[14].MLPhasbeenwidelyusedinmanyfields,suchaspatternrecognition,speechrecognition,andtimeseriesanalysis.Inthispaper,weuseMLPtogeneratewellloggingcurves.TheinputlayeroftheMLPconsistsofsevennodes,representingdifferentattributesofthewellloggingdata,includinggammaray,resistivity,density,neutron,deepresistivity,shallowresistivity,andspontaneouspotential.TheoutputlayeroftheMLPconsistsofonenode,representingthepredictedcurve.TheMLPhasthreehiddenlayers,eachhaving100,50,and25nodes,respectively.Theactivationfunctionofthehiddenlayersistherectifiedlinearunit(ReLU)function,whichcaneffectivelyimprovetheconvergencerateandtheclassificationperformanceoftheneuralnetwork[15].Theactivationfunctionoftheoutputlayeristheidentityfunction,whichisusedtoobtainthecontinuouspredictionvalues.3.3TrainingandvalidationTheproposedMLPneuralnetworkistrainedandevaluatedusingthewellloggingdatafromacertainoilfield.Thefirst90%ofthedataisusedfortraining,andthelast10%isusedforvalidation.Duringthetrainingprocess,themeansquarederror(MSE)isusedastheobjectivefunctiontooptimizetheneuralnetworkparameters.TheMSEmeasurestheaverageofthesquareddifferencesbetweenthepredictedandactualvalues.Thetrainingalgorithmusedinthispaperisthebackpropagationalgorithm,whichiswidelyusedinneuralnetworktraining.Thelearningrateofthebackpropagationalgorithmissetto0.01,andthebatchsizeissetto64.ThetrainingprocessstopswhenthevalidationMSEnolongerdecreases.Thetrainedneuralnetworkissavedandusedforwellloggingcurvegeneration.4.ExperimentalresultsandanalysisToevaluatetheperformanceoftheproposedmethod,wecomparethepredictedwellloggingcurveswiththeactualcurves.Thecomparisonisdoneintermsofthemeansquarederror(MSE),thecorrelationcoefficient(R),andthevisualization.TheMSEandRarecommonmetricstoevaluatetheaccuracyandprecisionofdataprediction.Table1.Comparisonofthepredictedandactualwellloggingcurves|Curve|TrainingMSE|ValidationMSE|Correlationcoefficient||------------|-------------|----------------|-------------------------||Gammaray|0.0023|0.0028|0.9751||Resistivity|0.0017|0.0021|0.9846||Density|0.0056|0.0063|0.9528|AsshowninTable1,thetrainingandvalidationMSEsofthepredictedcurvesareverysmall,whichmeansthattheneuralnetworkcanfitthedataverywell.Thecorrelationcoefficientsofthepredictedcurvesarecloseto1,indicatingthatthepredictedcurveshaveahighcorrelationwiththeactualcurves.Figure1.VisualizationofthepredictedandactualwellloggingcurvesAsshowninFigure1,thepredictedcurvesandtheactualcurveshav