基于信息融合理论和波动方程的地震地质统计学反演
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摘要
通过对地震、测井等地学信息的空间结构进行研究 ,为三维地质建模提供变差函数模型。针对中国陆相复杂油储地质特点 ,提出了有效的随机建模方法。实现了基于波动方程的地震地质统计学反演方法 ,获取了一系列波阻抗实现。采用信息融合方法 ,挖掘所有实现中的确定性信息。采用概率统计方法 ,研究反演结果的置信度。实现了高分辨率波阻抗成像
A study of signal spatial structures from seismic and logging data provides the varigram model for 3 D geologic model building. An efficient stochastic modeling method is proposed in this paper considering characteristics of complex reservoir geology in China. The seismic stochastic inversion based on the wave equation is developed and seismic impedance is obtained. Information from high confidence data sources is incorporated in the inversion by using information fusion algorithm. The confidence of inverted results is analyzed with probability statistic methodology. High resolution imaging of impedance is achieved.
A study of signal spatial structures from seismic and logging data provides the varigram model for 3 D geologic model building. An efficient stochastic modeling method is proposed in this paper considering characteristics of complex reservoir geology in China. The seismic stochastic inversion based on the wave equation is developed and seismic impedance is obtained. Information from high confidence data sources is incorporated in the inversion by using information fusion algorithm. The confidence of inverted results is analyzed with probability statistic methodology. High resolution imaging of impedance is achieved.
引文
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[10]杨绍国,贺振华.小波分析在地球物理信息融合中的应用[J].地球物理学报,2001,44(增刊):135-142
[2]HaasA ,DubruleO.Geostatistical inversion-Asequential m ethod of stochastic reservoir m odellingconstrained by seismic data[J].FirstBreak,1994,13(12):561-566.
[3]DubruleO,LamyP,RowbothamP S,et al.Nonuniqueness of seism ic inversion quantified bygeostatistics[A].The199860 thEAEGE AnnualMeeting[C].Leipzig.
[4]RowbothamP S,Lam yP,SwabyP A,et al.Geo-statistical inversion for reservoir characterisation[A].68thSEG AnnualMeetingExpandedAbstracts[C].NewOrleans:TheSociety ofExplorationGeo-physicists,1998.886-890.
[5]DubruleO,ThibautM,L am yP,et al.Geostatisti-cal reservoir characterisation constrained by3-D seis-m ic data[J].PetroleumGeoscience,1998,4(1):21-32.
[6]LamyP,Sw abyP A,RowbothamP S.From seism icto reservoir properties with geostatistial inversion[J].SPE ReservoirEval&Eny,1999,4(3):334-346.
[7]WaltzE,Llinas.MultisensorDataFusion[M ].Boston:ArtechHouse,1990.
[8]邵元,何发昌,罗志增.多传感器信息融合浅析[J].电子学报,1994,22(5):73-79.
[9]邢春晓.基于小波变换的多分类器融合分类系统[J].模式识别与人工智能,2000,13(1):22-28.
[10]杨绍国,贺振华.小波分析在地球物理信息融合中的应用[J].地球物理学报,2001,44(增刊):135-142
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