利用多分量地震数据预测油气藏分布
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摘要
多分量地震勘探提供了更多的油气藏地震物理参数信息,充分利用这些信息可以有效地预测油气藏分布。由于受测量不完整性、噪声以及处理过程中出现的误差等多种因素的影响,地震数据具有模糊性和对目标描述的不确定性。为了克服这种不确定性,基于模糊集理论,分别计算了油气藏和非油气藏的纵波速度、横波速度和泊松比的高斯类型隶属度函数。由测井数据计算的纵波、横波速度和泊松比的隶属度函数作为模型数据,建立由地震反演所得的这3个参数对油气藏支持程度的贴近度函数和基本可信度分配,然后根据证据融合规则将这些基本可信度分配合并成一个总体基本可信度分配,按照总体基本可信度分配最大的原则描述油气藏、非油气藏和不确定区。该方法用于四川广安气田,预测结果与钻井结果吻合很好。
The multi-component seismic technology can provide more seismic and physical parameters information used for predicting hydrocarbon distribution.The seismic data are of some illegibility and indeterminacy for description of object under the influence of acquisition and processing errors and noise.A fuzzy-evidential fusion method was presented to make the best information.On the basis of the fuzzy theory,the Gauss subordinate functions of P-wave and S-wave velocities and Poisson′s ratio were calculated.Taking the log subordinate function as model,the probable function was designed,then basic confidence level for the three parameters was obtained from seismic inversion.Three total basic confidence levels were obtained by fuzzy-evidential fusion and used to describe reservoirs with hydrocarbon or without hydrocarbon and indeterminacy area respectively.According to the maximal principle of total basic confidence level,the hydrocarbon distribution could be predicted.This method was applied to a gas field in Sichuan area.The predictions are consistent with the drilling results.
The multi-component seismic technology can provide more seismic and physical parameters information used for predicting hydrocarbon distribution.The seismic data are of some illegibility and indeterminacy for description of object under the influence of acquisition and processing errors and noise.A fuzzy-evidential fusion method was presented to make the best information.On the basis of the fuzzy theory,the Gauss subordinate functions of P-wave and S-wave velocities and Poisson′s ratio were calculated.Taking the log subordinate function as model,the probable function was designed,then basic confidence level for the three parameters was obtained from seismic inversion.Three total basic confidence levels were obtained by fuzzy-evidential fusion and used to describe reservoirs with hydrocarbon or without hydrocarbon and indeterminacy area respectively.According to the maximal principle of total basic confidence level,the hydrocarbon distribution could be predicted.This method was applied to a gas field in Sichuan area.The predictions are consistent with the drilling results.
引文
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[2]邓继新,王尚旭,李生杰,等.砂岩储层地震属性参数对孔隙流体的敏感性评价[J].石油学报,2006,27(6):55-59.Deng Jixin,Wang Shangxu,Li Shengjie,et al.Sensitivity evalua-tion of seismic attribute parameters for sandstone reservoir topore fluid[J].Acta Petrolei Sinica,2006,27(6):55-59.
[3]Foster D J,Smith S W,Dey-Sarkar D,et al.A closer look at hy-drocarbon indicators[R].Presented at the SEG Expanded Ab-stracts12,1993:731-735.
[4]Riedel M,Dosso S E,Beran L.Uncertainty estimation for ampli-tude variation with offset(AVO)inversion[J].Geophysics,2003,68(5):1485-1496.
[5]陈永仁,庞福民.储层横向预测油气的一种模式识别法[J].石油物探,1992,31(4):70-79.Chen Yongren,Pang Fumin.Pattern recognition for lateral oil/gas reservoir prediction[J].Geophysical Prospecting for Petrole-um,1992,31(4):70-79.
[6]刘震,张万选,张厚福.模式识别在预探井钻前储层预测中的应用[J].石油与天然气地质,1994,15(2):121-126.Liu Zhen,Zhang Wanxuan,Zhang Houfu.Application of patternrecognition in reservoir prediction in pre-exploratory wells beforedrilling[J].Oil&Gas Geology,1994,15(2):121-126.
[7]倪逸,郭玲萱,李萍.利用多种模式识别法预测廊固拗陷含油砂体[J].石油地球物理勘探,1995,30(2):264-271.Ni Yi,Guo Lingxuan,Li Ping.Predicting oil sand body in theLanggu depression by several pattern recognition methods[J].Oil Geophysical Prospecting,1995,30(2):264-271.
[8]吴大奎.利用K-L变化提高模式识别的精度[J].石油地球物理勘探,1995,30(2):272-276.Wu Dakui.Pattern recognition improvement using K-L transform[J].Oil Geophysical Prospecting,1995,30(2):272-276.
[9]施继承,聂勋碧.基于神经网络的油气预测系统[J].石油地球物理勘探,1996,31(5):685-692.Shi Jicheng,Nie Xunbi.Hydrocarbon prediction system based onneural network[J].Oil Geophysical Prospecting,1996,31(5):685-692.
[10]罗立民,王允成.自组织特征映射网络的改进及在储层预测中的应用[J].石油地球物理勘探,1997,32(2):237-245.Luo Limin,Wang Yuncheng.Improvement of self-organizingmapping neural network and the application in reservoir predic-tion[J].Oil Geophysical Prospecting,1997,32(2):237-245.
[11]Hampson D P,Schuelke J S,Quirein J A.Use of multiattributetransforms to predict log properties from seismic data[J].Geo-physics,2001,66(1):220-236.
[12]石玉梅,姚逢昌,甘利灯.多渠道数据融合及其应用[J].石油物探,2003,42(1):22-24.Shi Yumei,Yao Fengchang,Gan Lideng.Multi-wave data fusionand its applications in discrimination of fracture orientation[J].Geophysical Prospecting for Petroleum,2003,42(1):22-24.
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