砂體的地震信號特征檢測與解釋.pdf

1、同濟大學海洋地質(zhì)與地球物理系碩士學位論文砂體的地震信號特征檢測與解釋姓名：姜巖申請學位級別：碩士專業(yè)：固體地球物理指導教師：徐仲達牛彥良20010101TheDetectionandInterpretationoftheSandBodybytheSeismicCharactersAbstractInthepapertheseismicsimulationforthreedifferentgeologicalmodelofthereser

2、voirpattern，namelylentoidsandbodycuniformsandbodyandthininterbedsandbodyarecarriedonaccordingtothepracticereservoirparametersbasedonthewavetheoryTheeffectsofthestratumabsorption，randomnoise，especiallytheoverburdentransmi

3、ssiontotheseismic‘reflectioncharactersareanalyzed，Themultipleseismiccharacteristicattributesareextractedand9ptimizedfromseismogrambasedonthetechniqueoftheseismicattributesanalysis，Therelationshipbetweentheseismicattribut

4、esandthethicknessofthesandstone，theeffectsofthestratumabsorptionandthenoisetotheseismicattributes，therelationshipbetweentheoptimizedseismicattributesandthespatialdistributionofthesandstonearediscussedBasedOilthese，themet

5、hodoffuzzyneuralnetworksisputforwardedtopredictsandstonequantitativelythemodelandpracticedatashowsthatthemethodisavailableAnewwayofremovalofoverburdentransmissioneffectsbasedonthewellacousticlogisputforward，validatedbyth

6、etheorymodel，thetruereflected遺mplitudeoftheprimarylayerisrecoveredwellItisveryimportanttoincreasethefidelityoftheseismicsignalDuetothecomplexityofthegeologyconditions，itisverydifficulttousetheingleseismicattributetopredi

7、cttheparameterofthereservoibSOmultipleseismiccharacteristicattributes(SCA)areextractedandoptimizedTwooptimizedwayshavebeendeveloped：thefirstoneisprincipalcomponentanalysistoreducethedimensionsoftheseismicattributes；these

8、condisbased9nthegraycorrelationcoefficientbetweentheSCAandthereservoirgeologicalparametersPracticedataanalysisshowsthattheaveragepredictedaccuracyinthecourseofreservoirpredictionwiththeoptimumSCAhasincreasedby133percentt

9、hanthatoftheoriginalSCATheinnovativepointofthepaperis：1Themethodoftransmissioncoeflficientcompensationtoremovetheeffectsoftheoverburdenstratumisputforwardedandapplied，andtheseismictrueamplitudeisrecoveredwell；2Themethodo

10、ffuzzyneuralnetworksbasedontheprincipalcomponentanalysisisstudied，itcanincreasesthereliabilityofthequantitativereservoirpredictionKeywords：Waveequation，Seismiccharacteristicattributes，Transmission，F(xiàn)uzzyneuralnetworks，Res