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基于贝叶斯原理的PP波和PS波AVO联合反演方法研究(英文).docx

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基于贝叶斯原理的PP波和PS波AVO联合反演方法研究(英文) Introduction Effectiveimagingandcharacterizationofsubsurfacegeologicstructuresareessentialforexplorationanddevelopmentofoilandgasreservoirs.Amplitudevariationwithoffset(AVO)analysisisawidelyusedtechniqueforquantifyingrockpropertiesandfluidcontentofsubsurfaceformationsbasedonseismicwavereflectiondata.PPandPSwavesaretwotypesofseismicwavesthatarewidelyusedinAVOanalysis.PPwavespropagatethroughthesubsurfacebycompressingtherocklayerswhilePSwavespropagatethroughthesubsurfacebyshearingtherocklayers.Thedifferenceinthetraveltimesandamplitudesofthesewavesissensitivetorockpropertiesandfluidcontent. Inrecentyears,therehasbeenincreasinginterestinusingBayesianinversiontechniquesforAVOanalysis.Bayesianinversionisaprobabilisticapproachthatallowsuncertaintyintheinputdataandmodelparameterstobequantifiedandincorporatedintotheinversionprocess.Thisapproachcanleadtomorerobustandreliableresultscomparedtotraditionaldeterministicinversionmethods. Inthispaper,wepresentaBayesianinversion-basedAVOanalysisapproachthatcombinesPPandPSwavedataforsubsurfaceimagingandcharacterization.Theproposedmethodtakesadvantageofthecomplementaryinformationprovidedbythetwotypesofwavesandaccountsfortheuncertaintyintheinputdata.Theeffectivenessoftheproposedmethodisillustratedthroughacasestudy. Methodology TheproposedAVOanalysisapproachisbasedonaBayesianinversionframework.Theinversionproblemisformulatedasaprobabilisticinferenceproblem,wherethegoalistoinfertheposteriordistributionofthemodelparametersgiventheseismicdataandpriorinformation.Theposteriordistributionrepresentstheuncertaintyinthemodelparameters,whichcanthenbeusedtoestimatetherockandfluidpropertiesofthesubsurfaceformations. ThePPandPSwavedataareinvertedseparatelyusingalinearizedmodelthatrelatesthereflectivitytotherockandfluidproperties.ThereflectivityisassumedtobeaGaussianrandomvariablewithzeromeanandacovariancematrixthatdependsonthemodelparameters.ThemodelparametersincludetheP-waveandS-wavevelocities,Poisson'sratio,density,and