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1、第51卷第2期地球物理學報Vol.51,No.22008年3月CHINESEJOURNALOFGEOPHYSICSMar.,2008吳媚,符力耘,李維新.高分辨率非線性儲層物性參數(shù)反演方法和應用.地球物理學報,2008,51(2):546～557WuM,FuLY,LiWX.Ahigh2resolutionnonlinearinversionmethodofreservoirparametersanditsapplicationtooilPgasexploration.ChineseJ.Geophys.(inChinese),2008,51(2):546～557高分辨率
2、非線性儲層物性參數(shù)反演方法和應用112吳媚,符力耘,李維新1中國科學院地質與地球物理研究所,北京1000292中國海洋石油股份有限公司研究中心,北京100027摘要對于陸相沉積環(huán)境下的復雜隱蔽巖性儲層,由于觀測信息不準確,如信息重疊、信息缺失和噪音污染,以及巖石物理關系模糊等原因,儲層橫向預測存在不惟一性、不穩(wěn)定性和不確定性.基于線性假定的常規(guī)儲層橫向預測技術已不適用于復雜隱蔽巖性儲層的勘探.本文采用一種非線性儲層巖性物性褶積模型,建立波阻抗與孔隙度P泥質含量的函數(shù)關系;通過多級結構分解和雙向邊沿子波檢測來刻畫復雜巖石物理關系;通過Caianiello褶積神經(jīng)網(wǎng)絡實現(xiàn)
3、確定性反演、統(tǒng)計反演和非線性理論三者有機結合;最后聯(lián)合應用基于逆算子的反演方法和基于正算子的重建算法實現(xiàn)了綜合地質、測井和地震波阻抗信息進行高分辨率儲層物性參數(shù)反演.非線性儲層物性參數(shù)反演采用多井約束機制和分頻反演方式,在陸相和近海油氣勘探資料的實際應用中,取得了明顯應用效果.關鍵詞非線性反演,物性參數(shù),褶積模型,Caianiello褶積神經(jīng)網(wǎng)絡,邊沿檢測子波,分頻反演文章編號0001-5733(2008)02-0546-12中圖分類號P631收稿日期2007-07-10,2007-12-09收修定稿Ahigh2resolutionnonlinearinversion
4、methodofreservoirparametersanditsapplicationtooilPgasexploration112WUMei,FULi2Yun,LIWei2Xin1InstituteofGeologyandGeophysics,ChineseAcademyofSciences,Beijing100029,China2CNOOCResearchCenter,Beijing100027,ChinaAbstractInthepredictionofcomplexreservoirincontinentaldepositionenvironment,bec
5、auseofinexactdata(e.g.,information2overlapping,information2incomplete,andnoise2contaminated)andambiguousphysicalrelationship,inversionresultssufferfromnonuniqueness,instability,anduncertainty.Thus,reservoirpredictiontechnologiesbasedonlinearassumptionareunsuitedforthesecomplexareas.Byme
6、ansofnonlinearrockphysicalmodels,themethodpresentedinthepaperestablishesarelationshipbetweenimpedanceandporosityPclay2content.Throughmultistagedecompositionandbidirectionaledgewaveletdetection,itcandepictmorecomplexrockphysicalrelationship.Moreover,itusestheCaianielloneuralnetworktoimpl
7、ementthecombinationofdeterministicinversion,statisticalinversionandnonlineartheory.Last,itincorporatesgeologicalinformation,welldataandseismicimpedancetoperformpetrophysicalparametersinversionbycombinedapplicationsofmodel2basedanddeconvolution2basedmethods.Thejointinversioncons
