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1、第43卷第8期中南大學學報(自然科學版)、,01.43NO82012年8月JournalofCentralSouthUniversity(ScienceandTechnology)Aug.2012自適應最優(yōu)核時頻分布在地震儲層預測中的應用劉小龍,-,王華,趙淑娥,陳建軍。,魏軍,劉強(1.中國地質大學(武漢)資源學院構造與油氣資源教育部重點實驗室,湖北武漢,430074;2.中海油研究總院,北京,100027;3.中國石油玉門油田分公司勘探開發(fā)研究院,甘肅酒泉,7350101摘要:提高時頻分辨率是頻
2、譜成像技術研究的重點。自適應最優(yōu)核時頻分布采用隨信號特征自適應變化的核函數,在模糊域對遠離原點的互分量進行抑制,并盡可能的保留集中在原點附近的自分量。通過理論模型驗證,該方法較好地抑制了交叉項干擾,同時較連續(xù)小波變換和平滑偽Wigner-Ville分布等方法具有更高時頻分辨率。最后在營爾凹陷長沙嶺地區(qū)的實例應用中,利用自適應最優(yōu)核時頻分布對該區(qū)目標儲層進行了頻譜成像處理,并結合沉積相特征對長沙嶺地區(qū)進行有利區(qū)帶預測。結果表明:該方法適用于實際地震信號的時頻分析,對儲層刻畫優(yōu)于傳統(tǒng)方法,且對研究區(qū)儲層
3、預測具有有效性。關鍵詞:自適應最優(yōu)核時頻分布;高分辨率;頻譜成像;儲層預測中圖分類號:P631.4;TEl9文獻標志碼:A文章編號:1672—7207(2012108—3114一O7Applicationofadaptiveoptimalkerneltime—frequencyrepresentati0ninreservoirpredictionLIUXiao.1ong,一,WANGHua,ZHAOShu~e,CHENJian-jun,WEIJun,LIUQiang(1.KeyLaboratoryo
4、fTectonicsandPetroleumResourcesofMinistryofEducation,FacultyofEarthResources,ChinaUniversityofGeosciences,Wuhan430074,China;2.CNOOCResearchInstitute,Beijing100027,China;3.YumenPetroleumExplorationandDevelopmentInstitute,ChinaNationalPetroleumCorporatio
5、n,Jiuquan735010,China)Abstract:Toimprovere~utionisthekeypointofspectralimaging.Adaptiveoptimalkerneltime·frequencyrepresentationusedakernelfunctionwhichcanchangeadaptivelywithsignalcharacteristicstoweighttheambiguityfunction.Thecross—componentslocateda
6、wayfromtheoriginoftheambiguityplanearesuppressed,andtheauto—componentscenteredattheoriginarepassed.Adaptiveoptimalkerneltime—frequencyrepresentationdecreasestheimpactofcrosstermsobviouslyandobtainsabeRertime-frequencyresolutionthanthemethodsasCWTandSPW
7、VD,whichisprovedbythemodeldata.Basedonadaptiveoptimalkerneltime—frequencyrepresentation,theseismicdataofthetargetlayerinChangshalingareaofYing’erSagareprocessedbyspectralimaging.Onthebasisofspectralimagingresultsandsedimentaryenvironment,apredictionoff
8、avorablezonesinChangshalingareawasmade.Theresultshowsthatthismethodisnotonlysuitableforthetime—frequencyanalysisofseismicsignalandexcelsthetraditionalmethodsinreservoirdescription,butalsotakesaneffectivenessinreservoirprediction.Keyword