petrel建模培训2上课讲义

2.Faultframeworkmodeling

2.1

添加断层到断层格架模型2.Faultframeworkmodeling

2.2确定断层间关系2.Faultframeworkmodeling

2.3断层参数设置Demo

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Exercise3.TheframeworkfaultmodelisstoredundertheactivemodelintheModelspane.4.RuntheHorizonmodelingprocesslocatedundertheProcessespane>Structuralframework.5.Dropinnumberofrowsequaltotheamountofhorizoninput.Use,e.g.,surfacesstoredintheInputpaneasinput.SethorizontypesandclickOK.ResultsarestoredundertheHorizonsfolderundertheactivemodel.3.Horizonmodeling

3.1输入层面、地质分层、定义地层接触关系3.Horizonmodeling

3.2断层过滤参数设置6.ReopentheHorizonmodelingprocess,toggleontoApplygeologicalrulesandcreatezonemodel.ClickOK.QCtheHorizonsinthedisplaywindow,ifsatisfied,continuebuildingthezonesthroughtheHorizonmodelingprocess.7.Visualizethezonemodelinthedisplaywindow.3.Horizonmodeling

3.3应用地质规则并生成ZoneDemo

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Exercise4.StructuralGriddingStructuralGriddingFromStructuralFrameworktoafullypopulatedpropertymodelatanygridcellresolutioninjustafewclicks!Fullystair-steppedgridmatchingtheStructuralFrameworkfaultsandhorizonsExcellentgeometryevenalongthefaultsAllowsmorecomplexmodelingStructuralFrameworkModelingorreservoirengineeringview-flexibility&deeperintegration4.StructuralGridding----ProcessInputStructuralFrameworkGridnameLayerpinch-outsettingsZonationsettingsandoptionalforcedwell-tieLayeringsettingsbyzoneOptionalAOI:enablesgridcreationonasub-setoftheStructuralFrameworkGridgeometrysettingsFaultstab:includeorexcludeStructuralFrameworkfaultsinsidethegridOutput六、测井曲线粗化基于Studio知识库的Petrel一体化研究流程测井曲线粗化测井曲线粗化QCNY三维网格QCNY层位编辑断层建模构造格架网格化建立地层格架地层格架垂向网格化纵向网格化地层厚度网格化井点地层厚度计算网格几何属性建模网格体积

QC

NY网格正交性QC

N地层格架QCNY构造解释Y岩相解释岩相建模属性建模数据分析储量计算模型粗化岩相QCNY属性QCNY地层划分与对比合成地震记录速度建模时深转换油藏数模模型历史拟合方案调整、加密井网预测模拟经济评价油田动态管理井历史生产数据吻合储量吻合YNYN地震属性分析数据查询与研究工区生成InputOutputStructuralframeworkgridFaultModelThisprocessalowstheusertocreateageocellulargridwhichcanbelaterpopulatedwithpropertiesPillarGriddingMakeHorizons输出断层模型到角点网格模型2.测井曲线粗化过程五、测井曲线粗化3.测井曲线粗化质量控制1.基本概念1.基本概念

OverviewCellsexistalongthewellpath.Valuesassignedtocellsbasedonwelllogvaluesalongthewellpath.Upscaledlogsusedtofillinthe3Dgrid.TheScaleupwelllogsprocessaveragesthevaluestothecellsinthe3Dgridthatarepenetratedbythewells.Eachcellgetsonevalueperupscaledlog.ThesecellsarelaterusedasastartingpointforPropertymodeling.NOTE:Anupscaledpropertywillhavea[U]followingitsnameinthePropertyfolderintheModelspane.1.基本概念

AveragingmethodsforDiscretelogsAveragingmethodDescriptionMostofWillselectthediscretevaluewhichismostrepresentedinthelogforeachparticularcellMedianWillsorttheinputvaluesandselectthecentervalue.Minimun/MaximumSamplesthemin/maxvalueofthewelllogforthecell.MidPointPickWillpickthelogvaluewherethewellishalfwaythroughthecell.

RandomPickPicksalogpointatrandomfromanywherewithinthecell.

1.基本概念

AveragingMethodsforContinuousLogsAveragingmethodDescriptionArithmeticmeanTypicallyusedforadditivepropertiessuchasporosity,saturationandnet/gross.HarmonicmeanGivestheeffectiveverticalpermeabilityifthereservoirislayeredwithconstantpermeabilityineachlayer.Itworkswellwithlognormaldistributions.Itissensitivetolowervalues.GeometricmeanNormallyagoodestimateforpermeabilityifithasnospatialcorrelationandislognormallydistributed.Itissensitivetolowervalues.MedianWillsorttheinputvaluesandselectthecentervalue.RMS(RootMeanSquared)Willprovideastrongbiastowardshighvalues.Minimun/MaximumSamplesthemin/maxvalueofthewelllogforthecell.MidPointPickWillpickthelogvaluewherethewellishalfwaythroughthecell.

RandomPickPicksalogpointatrandomfromanywherewithinthecell.

ArithmeticmeanSandstoneporosityistypicallynormaldistributedGeometricmeanHarmonicmeanShalepermeabilityistypicallygammadistributed1.基本概念测井曲线分布概率与粗化平均方法选择1.基本概念测井曲线分布概率与粗化平均方法选择岩相：Mostof孔隙度：Arithmetic渗透率：HormonicGeometry1.基本概念

BiasingtoaDiscreteLogRawporosityUpscaledfaciesUpscaledporosityRawfaciesSandShale1.基本概念

TreatLogaslinesorpointsTreatlogDescriptionAspointsAllsamplevalueswithineachcellareusedforaveraging.AsLinesIfthemidpointofalinebetweentwosamplepointsisinsideacellthepointoutsidethecellwillbeusedinthecalculation.1.基本概念

MethodofwhichpenetratedcellstouseMethodDescriptionSimpleAllcellspenetratedbythewelltrajectoryareincludedThroughcellThewelltrajectorymustpenetratetwooppositecellwallsNeighborcellAllpenetratedcellsareusedbutcellsinthesamecelllayerareaveraged.2.测井曲线粗化过程

HowtoScaleupWelllogs1.Createnewproperty3.Select

log

fromthedrop-downmenu(capturedfromInputpane>Globalwelllogsfolder)4.SelectthebestscaleupSettings

dependingonthetypeoflog(continuousordiscrete).2.SelectInputfrom:Welllogs.5.ClickApplyorOK.2.测井曲线粗化过程

AveragingDiscreteLogsDiscretelogs,suchasfacies,haveonlyintegervalues(0,1,2etc.).2.TheAveragemethodisnormallyMostof(willusethevaluewhichismostrepresentedinthecell).3.Insomesituations,itmaybenecessarytousetheweightingoption.ClickUseweightingandfillinWeightedtab.231.SelectinputfromaFacieswelllogtocreateanewproperty.132.测井曲线粗化过程

AveragingContinuousLogsContinuouslogs,suchasporosity,haverealnumbervalues(decimals).4.YoucanspecifythatthereshouldbeaMinimumnumberofwelllogsamplesinacellforittobeincludedintheupscaling.2.SelecttheAveragemethod(hereArithmeticforporosity)3.Itispossibletobiastoanalreadyupscaledfacieslog.1.SelectinputfromacontinuousWelllogtocreateanewproperty(here,porosity).1234Demo

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Exercise3.测井曲线粗化质量控制3.测井曲线粗化质量控制3.测井曲线粗化质量控制基于Studio知识库的Petrel一体化研究流程测井曲线粗化测井曲线粗化QCNY三维网格QCNY层位编辑断层建模构造格架网格化建立地层格架地层格架垂向网格化纵向网格化地层厚度网格化井点地层厚度计算网格几何属性建模网格体积

QC

NY网格正交性QC

N地层格架QCNY构造解释Y岩相解释岩相建模属性建模数据分析储量计算模型粗化岩相QCNY属性QCNY地层划分与对比合成地震记录速度建模时深转换油藏数模模型历史拟合方案调整、加密井网预测模拟经济评价油田动态管理井历史生产数据吻合储量吻合YNYN地震属性分析数据查询与研究工区生成修改纵向网格大小修改测井曲线粗化平均算法垂向网格分辨率与垂向变程分析结合多井的岩相垂向变化情况，进行垂向变程分析，确定垂向变程

对复杂的薄互层地层，可以参照薄互层的厚度，确定垂向变程

三维网格的垂向分辨率应该小于垂向变程的一半，即薄层、夹层在纵向上至少有两个单元格。如果不满足，重新进行垂向网格化Layering七、相建模与数据分析基于Studio知识库的Petrel一体化研究流程测井曲线粗化测井曲线粗化QCNY三维网格QCNY层位编辑断层建模构造格架网格化建立地层格架地层格架垂向网格化纵向网格化地层厚度网格化井点地层厚度计算网格几何属性建模网格体积

QC

NY网格正交性QC

N地层格架QCNY构造解释Y岩相解释岩相建模属性建模数据分析储量计算模型粗化岩相QCNY属性QCNY地层划分与对比合成地震记录速度建模时深转换油藏数模模型历史拟合方案调整、加密井网预测模拟经济评价油田动态管理井历史生产数据吻合储量吻合YNYN地震属性分析数据查询与研究工区生成六、相建模与数据分析2.相数据分析2.1垂向相比例分析2.2相厚度分析2.3变程分析1.基本概念3.相建模3.1

Common设置3.2

Zone

Setting3.3相模拟参数设置1.基本概念

OverviewWhybuildFaciesmodels?TounderstandgeologicalprocessesTocapturefaciesarchitecture,suchasreservoirconnectivityandhighlevelheterogeneityHonordescriptivefaciesinformation:shape,size,orientation,proportion,distribution,statistics…Identifyfaciesfeaturescriticaltoproduction.1.基本概念

相建模方法选择Ifwelllogsareup-scaled,theycanbeusedinDeterministicandStochasticmethods.Ifnowelllogsareavailable,mostDeterministicmethodscannotbeused(apartfromAssignvalues,CalculatorandInteractivedrawing),andmainlyUnconditionalStochasticmethodsareused.DeterministictechniquesAretypicallyusedwhendensedataisavailable(manywellsorwells+seismic).Yieldasingleestimatedresult.StochastictechniquesAretypicallyusedwhensparsedataispresent.Canproducemultipleequallyprobablerealizations(outputs).PropertymodelinggeneralworkflowExplorationAppraisalDevelopmentProductionLessdataMoreuncertaintyMoredataLessuncertaintyDeterministicAddressedPixelbasedInterpolationEstimationObjectbasedStochastic1.基本概念

Faciesmodelingmethods-overviewDeterministicLearningsystemEstimationDirectAddressingArtificialIndicatorKrigingAsignvaluesInteractiveNeuralNetDiscretedistributionofthepropertyhonoringthepre-definedhistogramChoosefromundefined,constant,otherproperty,surfaceandverticalfunction.Allowstheusertopaintfaciesdirectlyonthe3Dmodel.UsestheclassificationmodelmadeintheTrainEstimationModel.1.基本概念

Faciesmodelingmethods-overviewDeterministicLearningsystemEstimationDirectAddressingArtificialIndicatorKrigingAsignvaluesInteractiveNeuralNet1.基本概念

Faciesmodelingmethods-overviewStochasticPixelbasedObjectbasedSequentialIndicatorSimulationTruncatedGaussianSimulationTruncatedGaussianSimulationwithtrendsMulti-pointFaciesSimulationObjectModelingDistributestheproperty,usingthehistogram.Directionalsettings,suchasvariogramandextensionaltrends,arealsohonored.Usedmostlywithcarbonateswherefaciesareknowntobesequential,itdealswithlargeamountsofinputdata,suchasglobalfractionsandtrends.Distributesthefaciesbasedonatransitionbetweenfaciesandtrenddirection.ThetrendsareconvertedintoprobabilitiestothenrunTGS.Thevariogramisreplacedbyatrainingimagegivingboththefaciesandtherelativepositiontoeachother,describingthespatialcorrelationfromone-to-multiplepoints.Allowstopopulateadiscretefaciesmodelwithdifferentbodiesofvariousgeometries,faciesandfraction1.基本概念

Faciesmodelingmethods-overviewStochasticPixelbasedObjectbasedSequentialIndicatorSimulationTruncatedGaussianSimulationTruncatedGaussianSimulationwithtrendsMulti-pointFaciesSimulationObjectModeling1.基本概念

Petrel离散属性建模技术PixelBased:ShapedecidedbyVariograms,trends,etc.SISIMTGSIM

TGSIMwithtrendsObjectBased:Facieswithdefinedgeometricshapes.GeneralobjectFluvialAdaptiveChannel1.基本概念

WellCorrelationandFaciesInterpretationAxialZone

AZone

BOff-axialOff-axial1.基本概念

Carbonates1.基本概念

DepositionalEnvironments-CarbonatesCarbonatesareformedinshallowseascontainingfeaturessuchasreefs,lagoons,andshore-bars.CarbonateporosityInterparticleporosityIntergranularporosityIntercrystalineporosityMoldicporosity1.基本概念

ClasticsONSHOREEolianLacustrineFluvialFloodplainLakeEoliandunesFandeltaSHORELINE/TRANSITIONDeltafrontDELTASLOPEOFFSHORESHELFDeltaslopeturbiditesOffshore/shelfturbiditesSlope/ShelfDeltaslopemassflowsMeanderingriverSplayAlluvialfanBraidedriverFandeltafront/prodeltaShorelinedeposits1.基本概念

MarineThedepositionalenvironmentcanbelakeorcontinental,shallowordeep

water

marine.Theenvironmentdeterminesmanyofthereservoircharacteristics.1.基本概念

ContinentalContinentaldepositscanbeeoliansanddunes,alluvialfans,etc.Ashallowmarineenvironmenthasalotofturbulence,hencevariedgrainsizes.Itcanalsocontaincarbonatesandevaporites.Adeepmarineenvironmentproducesfinesediments.Sandstoneporosity六、相建模与数据分析2.相数据分析2.1垂向相比例分析2.2相厚度分析2.3变程分析1.基本概念3.相建模3.1

Common设置3.2

Zone

Setting3.3相模拟参数设置2.相数据分析

StatisticalDiscreteDataAnalysisInDataanalysisprocessfordiscretepropertiesthefollowingfunctionalitiesareavailable:FaciesProportion:verticalfaciesvariationFaciesThickness:thicknessofindividualfaciesintervalsFaciesProbability:

calibrationwithsecondaryattributesDiscreteVariogram:spatialfaciescontinuityDataanalysisisaprocessofdataQC,understandingthedata,andpreparinginputsforPropetymodeling2.1垂向相比例分析Probabilityvalues(editable)FitprobabilitycurveTofractionhistogramProportion

:AppliedasverticalprobabilitycurvesbasedontheoriginalfractionoffaciesineachK-layer.Theprobabilityofusingthegivenfractionsisgivenbyacurvethatcanbeeditedmanually.Channel

and

levee

faciesareabundantintheshallowerportionoftheinterval,while

Lobe

faciesisconcentratedinthelowerportionofthezone.2.2相厚度分析Thickness:Histogramviewofthicknessvariationsofeachfaciestype.Thebinintervalcanbespecifiedinprojectunitstoincrease/decreasetheresolution.Add/removefaciescodesDisplayupscaled/rawlogsWiththegivenBinintervalof4m:Channel

and

levee

faciesvarybetween4to28m.Lobe

faciesvariesfrom12to32m.VerticalMajorMinorYouwillneedthreedirections:Twointhehorizontal(majorandminor)andoneintheverticaldirectionThevariogramquantifiesthespatialcontinuityofthedataTherangepointsthedistancefromwhichabove,thespatialdependenceissettorandomnessSeparationdistance(lag)RangeSillNuggetVariance12345Variogramiscalculatedin3directionsVariogram¶metersTherearemanyvariogramtypeswhichcanbefitintothedata.Petrelprovidesthreeoptionsofprominenttypesincludingexponential,sphericalandgaussianvariogramsvariogramsThe

azimuth

istherotationangleofthemajorrange2.3变程分析2.3变程分析Variogram:Variogramsshouldbemodeledforeachfaciesasfaciesvaryincorrelationlengthwithdistance.Howbundledthesestochasticallymodeledpixelfaciestypesappearisdependantonvariogramrangeandvariance(nugget).FaciestypeSearchconesetup–togeneratethesamplevariogramVariogramresults–tobeusedinModelingPoints=SamplevariogramLine=RegressionlineLine=ModelVariogramHistogram=Numberofpairs2.3变程分析变程平面图地震属性3.相建模3.1

Common设置3.2

Zone

Setting3.3相模拟参数设置六、相建模与数据分析2.相数据分析2.1垂向相比例分析2.2相厚度分析2.3变程分析1.基本概念3.1

CommonSettingsUsefilter–Shouldbeselectedonlyifafilteredpartofthegridistobemodeled.Ensurethatallcellsgetavalue–ifnoinputdata,allcellswillbepopulatedbyaveragingsurroundingcells.Overwrite–Willoverwritethepreviousrealizationswithsamesuffixnumber.Numberofrealizations–whenrunningUncertaintyanalysis,multiplerealizationsaremadewiththesameinputdata.Localmodelupdate–updatesthemodelinsidearegion,insideapropertyoraroundawell3.2

ZoneSettingsZone–Clicktoactivatezonation;selectzonetomodelfromdrop-downlist.Facies–Ifconditioningtoapreviousfaciesmodel,clickalsotheFaciesbutton.Method–Selectappropriatemethodfromthedrop-downlistforthezonetobemodeled.Lock–‘Leavezoneunchanged’;unlocktoactivatezonesettings.3.3相模拟参数设置1.Selectanupscaledproperty:(U)assuffix.4.SelecttheFaciesfromthetemplate.Usethebluearrowtoinsertthemintothemodel.5.Variogram:A.SpecifyRange,NuggetandTypemanuallyB.orgetavariogramfromDataAnalysis6.Fraction:A.UseGlobalfractionfromUpscaledcells.B.…oruseprobabilities(property/trend).C.UseattributeprobabilitycurvesorverticalproportioncurvesfromDataanalysis.SISisapixelbasedmodelingalgorithm,usingupscaledcellsasbasisforfractionoffaciestypestobemodeled.Thevariogramconstrainsthedistributionandconnectednessofeachfacies.3.SelectSISastheMethodforonezone.2.Selectthezonetomodelandunlockit.3.3相模拟参数设置八、属性建模与数据分析基于Studio知识库的Petrel一体化研究流程测井曲线粗化测井曲线粗化QCNY三维网格QCNY层位编辑断层建模构造格架网格化建立地层格架地层格架垂向网格化纵向网格化地层厚度网格化井点地层厚度计算网格几何属性建模网格体积

QC

NY网格正交性QC

N地层格架QCNY构造解释Y岩相解释岩相建模属性建模数据分析储量计算模型粗化岩相QCNY属性QCNY地层划分与对比合成地震记录速度建模时深转换油藏数模模型历史拟合方案调整、加密井网预测模拟经济评价油田动态管理井历史生产数据吻合储量吻合YNYN地震属性分析数据查询与研究工区生成七、属性建模与数据分析2.属性数据分析2.1数据变换2.2变程分析1.基本概念3.属性建模3.1属性建模输入数据类型3.2

Common和Zone

Setting3.3相模拟参数设置3.4属性质量控制3.5属性计算器1.基本概念

OverviewKeyIssuesDifferentpetrophysicalpropertydistributionsindifferentfaciesVarioustrendsSpatialvariationforeachpetrophysicalparameterCorrelationbetweenparametersIdentifypetrophysicalfeaturescriticalto

production1.基本概念

PetrophysicalmodelingmethodsDeterministicEstimationInterpolationKrigingInterpolationKrigingKrigingbyGSLIBClosestFunctionalMovingaverageHonorswelldata,inputdistributions,variogramsandtrends.Itcanworkinrealcoordinatesandit’sfast.Itperformsfastest.Ithasaco-krigingoptionandallowsusertochoosebetweensimpleandordinarykriging.Ithastheoptionofcollocatedco-krigingandyoucanchoosebetweenordinaryorsimplekriging.Itusestheclosestwelldatainputforeachunsampledlocation.Ihonorswellandtrenddatacreatinga3Dfunction(parabolic,simpleparabolic,planarorbi-linear)usedintheinterpolation.Basedontheinputitgivesanaveragevalueandcalculatestheweigthsaccordingtothedistancefromwells.DeterministicEstimationInterpolationKrigingInterpolationKrigingKrigingbyGSLIBClosestFunctionalMovingaverage1.基本概念

PetrophysicalmodelingmethodsDeterministicLearningsystemStochasticDirectAddressingArtificialPixelbasedAssignvaluesNeuralNetSequentialGaussianSimulationGaussianRandomFunctionSimulationChoosefromundefined,constant,otherproperty,surfaceandverticalfunction.UsestheclassificationmodelmadeintheTrainEstimationModel.ItcanbeusedHonorswelldata,inputdistributions,variogramsandtrends.Thevariogramanddistributionareusedtocreatelocalvariations,evenawayfrominputdata.ItisfasterthanSGS,andgivesbettervariogramreproduction.andhasafastcollocatedco-simulationoption.1.基本概念

PetrophysicalmodelingmethodsDeterministicLearningsystemStochasticDirectAddressingArtificialPixelbasedAssignvaluesNeuralNetSequentialGaussianSimulationGaussianRandomFunctionSimulation1.基本概念

Petrophysicalmodelingmethods七、属性建模与数据分析2.属性数据分析2.1数据变换2.2变程分析1.基本概念3.属性建模3.1属性建模输入数据类型3.2

Common和Zone

Setting3.3相模拟参数设置3.4属性质量控制3.5属性计算器连续属性数据分析InDataanalysisprocessforcontinuouspropertiesthefollowingfunctionalitiesareavailable:Datatransformation:datadistributionandspatialtrendsVariogramanalysis:spatialvariationCorrelation:relationshipbetweenparametersByinterval(zone)andbyfacies:maintainheterogeneityanddifference.DataanalysisisaprocessofdataQC,understandingthedataandpreparinginputsforPropertymodeling.HistogramfordifferentFacies:Isthehistogramnaturalordoesitneedtobeedited?InputdistributionforonefaciestypeLobePhi=0.10ShalePhi=0.02ChannelPhi=0.222.1数据变换

DataAnalysisProcess:Distribution(byIndividualFacies)2.1数据变换

WhatisaTransformation?Inputdistribution(welllogs)m=0,s=1NormalscoretransformationOutputdistribution(3Dproperty)Back-transformationTransformation

isthepreparationofarealdatasetintoaninternaldataset.Severaltransformationscanberuninsequence.Beforeasimulationalgorithmisrun,afinalNormalScore

transformationisused(standardnormaldistribution:Mean=0,Std.dev=1).Back-transformationwillautomaticallybeperformed

inthereverseorderoftheinitialtransformationstopreservethespatialtrendsandoriginaldatadistributionintheresultingproperty.TruncatedistributionrangeTrucatestheinputdistributiontodeleteunrepresentativevaluesorpushthemtonextbinTheOutputtruncationhasnoHistogramrepresentationasitisperformedaftersimulationasalimitontheCDFcurveusingamax/min(notaphysicalcutofinputdata)AnomalousGammadistributionforporosityinSand(cementation)Truncatestheoutputofarealizationonback-transformationofdata,togetvaluesinadesiredrange2.1数据变换

DataAnalysisProcess:Transformation(Distribution)NoinputparametersrequiredRemovesskewnessofdataby

aLambdafactortoapproximateNormaldistributionChangedistributionshapeLognormaldistributionofPermeabilityLobefacies(ZoneB)GammadistributionPermeability2.1

数据变换

DataAnalysisProcess:Transformation(ShapeandScale)Changedistributionrange&scaleShiftdatabymeanandscalebystandarddeviation,doneafterspatialtransformations.Forcesanydistributionshapetostandardnormal(m=0,std=1)Meanpor=0.21Meanpor=0.11Newtargetmean=Inputmeanshift-realmean/std.devDistributioncurvecanbeeditedwithcaution2.1数据变换

DataAnalysisProcess:Transformation(DistributionShift/Scale/Shape)Betadistributioncontrolbyalphaandbeta

parameters.BetadistributionisanalternativetoNormalscore.Aftersimulationtargetdistributionisrespected.Changedistributionrange&scaleGeneraldistributionisanotheralternativetoNormalscoreanditneedsasinputadistributionfunction.2.1数据变换

DataAnalysisProcess:Transformation(DistributionScale/Shape)Beforemodeling,Petrelwillperformthefollowingtransformations:Truncatetheinputdistribution(i.e.eliminateoutliers)Removethe1Dtrend(verticalcompaction)Normalscorethedata(meanof0,stdof1)Performmodelingbasedonthetransformeddataset.Thenback-transformthedata:RemovetheNormalscoretransformAddthe1DtrendthatwasremovedTruncatetheoutputdistribution(usingsetMax.andMin.values)2.2变程分析

ExampleofaTransformationSequence(PorosityModeling)2.2

变程分析Points=SamplevariogramLine=RegressionlineLine=ModelVariogramHistogram=NumberofpairsThevariogramiscalculatedonupscaledtransformeddatainsimboxmode.Itmeasuresvariabilitywithdistance.Calculatedin3directions:HorizontalMajorHorizontalMinorVerticalAsearchconemustbesettocapturedatawithinlags.2.2变程分析

DataAnalysisProcess:VariogramAnalysis3.属性建模3.1属性建模输入数据类型3.2

Common和Zone

Setting3.3相模拟参数设置3.4属性质量控制3.5属性计算器七、属性建模与数据分析2.属性数据分析2.1数据变换2.2变程分析1.基本概念3.1属性建模输入数据类型Welldata:upscaled/blockedwelllogsDistribution:histogramVariogram(spatialmodel):

-Direction,modeltype,nuggetandsill -Correlationlengthsin3directions(range)Faciesmodel:ConditioningSpatialtrends:Fromseismic/analogsetc.Secondaryparameter:withacorrelation3.2

Common和Zone

SettingTwoMainModelingSettingsbuttonsareavailable(CommonandZonesettings).TheseworkinexactlythesamewayasforFaciesmodeling:3.3相模拟参数设置

GaussianRandomFunctionSimulation(GRFS)GRFSisthein-housestochasticmodelingalgorithmforPetrophysicalmodeling.ItisaverygoodalternativetoSequentialGaussianSimulation(developedbyGSLIB)GRFSisbasedontheideaof:ConditionalSimulation=Kriging+UnconditionalsimulationGRFSisparallelizedand,therefore,muchfasterthansequentialalgorithms.GRFSgivesabetterVariogramreproductionthanSGS.GRFShasacollocatedco-krigingoption,allowingfastupdatesoftheresultswhenchangingthecorrelationcoefficient(interactiveslide-bar).3.3相模拟参数设置

StochasticModelingusingGRFSconditionedtoaFaciesModel1.Selectanupscaledproperty:(U)assuffix.2.Selectthezonetomodelandunlockit.5.Variogramtab:A.SpecifyRange,NuggetandTypemanuallyB.orgetavariogramfromDataAnalysisGaussianRandomFunctionSimulationisamuchusedstochasticmodelingalgorithm.Ithonorswelldata,inputdistributions,variogramsandtrends.Itwillcreatemultipleequallyprobableoutputs.4.SelectGaussianrandomfunctionsimulationasthemethodforthezone.3.ClickontheFaciesbuttonandselectthefaciesmodelifconditioningisnecessary.3.3相模拟参数设置

StochasticmodelingusingGRFS:Distribution8.SettheMinimumandMaximumvalueoftheOutputdatarange(absoluteorrelative%).9.DefinetheshapeoftheDistribution:UseFromupscaledlogswhenavailable.ABivariatedistributionwilluseaSecondarypropertyonwhichtobasethedistribution.Usedifupscaledlogs6.SelectIslogarithmicifthepropertyhasalogarithmicbehavior.Usedifno/fewupscaledlogsFromdistributionfunction7.YoucanmanuallyspecifytheSeednumber.IntheDistributiontabthefollowingsettingscanbefound:3.3相模拟参数设置

StochasticModelingusingGRFS:usingaSecondaryVariable1.DefinetheSecondaryvariableA-Property:Usuallyacontinuousseismicattributevolumethatcorrelateswiththepropertytobemodelled.B-AHorizontalsurfaceC–Verticalfunction2.ChoosetheMethodA-Localvaryingmeanwillnormaltransformthedatabeforeaddingtheresidual.B-Collocatedco-krigingwilluseacorrelationcoefficienttocalculatethecontributionofthesecondaryvariable.Youcansteerthesimulationusingthespatialdistributionofasecondaryvariabletogetherwithacorrelationcoefficient(Collocatedco-kriging).3.4属性质量控制Filterthehistogramby:1.UseZonefilter.2.FilteronotherpropertyvaluesbyclickingthefilterbuttonandgotopropertyfilterinthesettingsforthePropertiesfolder.QCresultsinahistogram:GotothesettingsforthePropertyandselecttheHistogramtab.Checkthatthehistogramfollowsthedistributionfrom:RawlogsUpscaledcells

3Dgrid123.5

属性计算器

PropertycalculatorThepropertycalculatorcanbeusedtomodifyalreadyexistingpropertiesortomakenewproperties.StatisticalfunctionsGeometricalfunctionsLogicalstatementsMathematicalfunctions3.5

属性计算器

HowtousethePropertyCalculatorFormulabarDatalist1.Right-clickthePropertiesfolderandselectCalculator.2.Typeaformula.Useexistingpropertiesfromthedatalist.Usethekeypadsoftheinterface.4.Selectapropertytemplate.3.Alternatively,browseforafiletobeused.5.RunyourfileorclickENTERtoperformanyformula.23455HistoryarchiveNOTE:Youcanreuseanyformulabyselectingi