基于地震属性的储层预测方法——以永安地区永3区块沙河街组二段为例
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摘要
针对永安地区永3区块沙二下段沉积微相和砂层厚度特征,提出了利用地震数据进行目标属性预测的新方法。传统的储层预测方法通过单属性简单交汇进行,预测精度较低。概率神经网络首先通过选取褶积算子解决测井参数与地震数据之间不等频现象,并利用交叉验证法检验储层预测结果的可靠性。交叉验证过程中,将测井数据分为训练数据组和验证数据组两部分,训练数据组用来进行属性转换,验证数据组用来验证结果的预测误差。对属性进行变换时,用校验误差来衡量属性变换和选取的有效性。在此基础上提取出对孔隙度敏感的5种属性。通过预测误差和校验误差计算及预测孔隙度和实际孔隙度值的相关性分析,对预测精度进行评价。最后利用PNN神经网络对永安地区永3区块沙二下段砂层进行储层物性预测,获得了目标地层的主要储层物性参数,取得了良好的预测效果。
A new method is described for predicting reservoir properties using seismic data based on sedimentary micro-facies and sand thickness features of ES2x in Yong 3 block of Yong'an area.Conventional reservoir predicting method is to cross-plot the target data and seismic attribute for deriving the desired relationship between the two,and has a low predictive precision.Probabilistic neural network (PNN) method uses the convolutional operator to resolve the frequency difference between seismic attribute and the log data.The reliability of reservoir predicting results can be checked by cross-validation.Cross-validation divides the entire training data set into two subsets:the training data set and the validation data set.The training data set is used to derive the transform,while the validation data set is used to measure its final prediction error.Validation error can be used to check the validity of the attributes transform.On the basis of above research,6 optimum attributes susceptive to porosity are selected.Predicting precision is appraised by calculating prediction error and validation error and by correlating the prediction porosity and actual porosity.The reservoir porosity of sand layer of ES2x in Yong 3 block of Yong'an area is predicted successively by using PNN at last.
A new method is described for predicting reservoir properties using seismic data based on sedimentary micro-facies and sand thickness features of ES2x in Yong 3 block of Yong'an area.Conventional reservoir predicting method is to cross-plot the target data and seismic attribute for deriving the desired relationship between the two,and has a low predictive precision.Probabilistic neural network (PNN) method uses the convolutional operator to resolve the frequency difference between seismic attribute and the log data.The reliability of reservoir predicting results can be checked by cross-validation.Cross-validation divides the entire training data set into two subsets:the training data set and the validation data set.The training data set is used to derive the transform,while the validation data set is used to measure its final prediction error.Validation error can be used to check the validity of the attributes transform.On the basis of above research,6 optimum attributes susceptive to porosity are selected.Predicting precision is appraised by calculating prediction error and validation error and by correlating the prediction porosity and actual porosity.The reservoir porosity of sand layer of ES2x in Yong 3 block of Yong'an area is predicted successively by using PNN at last.
引文
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[2]习陈军,陈岩.地震属性分析在储层预测中的应用[J].石油物探,2001,40(3):94-100.[XI Chenjun,CHEN Yan.Application of seismic attribute analysis in reservoir prediction[J].Geo-physical Prospecting for Petroleum,2001,40(3):94-100].
[3]倪逸,杨慧珠,郭玲萱,等.储层油气预测中地震属性优选问题探讨[J].石油地球物理勘探,1999,34(6):614-626.[NI Yi,YANG Huizhu,GUO Lingxuan,et al.Opti mization problemof seismic attributes in hydrocarbon reservoir prediction[J].Oil Geophysical Prospecting,1999,34(6):614-626].
[4]刘文岭,牛彦良,李刚,等.多信息储层预测地震属性提取与有效性分析方法[J].石油物探,2002,41(l):100-103.[LI U Wen-ling,NI U Yanliang,LI Gang,et al.Seismic attribute extraction and effectiveness analysis of multi-attribute reservoir prediction[J].Geophysical Prospecting for Petroleum,2002,41(1):100-103].
[5]王忠.用于储层预测和监测的地震属性技术[J].石油物探译丛,1997(6):27-32.[WANG Zhong.Seismic attribute technol-ogy for reservoir forecasting and monitoring[J].Overseas Petrogeophysics,1997(6):27-32].
[6]秦月霜,陈显森,王彦辉.用优选后的地震属性参数进行储层预测[J].大庆石油地质与开发,2000,19(6):44-46.[QI N Yue-shuang,CHEN Xiansen,WANG Yanhui.Performing a reser-voir prediction with opti mized seismic attribute parameters[J].Petroleum Geology&Oilfield Development in Daqing,2000,19(6):44-46].
[7]乐有喜.利用模型技术研究地震属性的地质意义[J].物探与化探,2001,25(3):191-197.[LE Youxi.The application of model technology to studying geological research geological signifi-cance of seismic attributions[J].Geophysical and Geochemical Exploration,2001,25(3):191-197].
[8]黄真萍,王晓华,王云专.地震信息的属性参数提取和砂体预测方法[J].石油地球物理勘探,1997,32(5):669-682.[HUANG Zhenping,WANG Xiaohua,WANG Yunzhuan.Method for making both seismic attributive parameter extraction and sand body prediction[J].Oil Geophysical Prospecting,1997,32(5):669-682].
[9]席裕庚,柴天佑,恽为民.遗传算法综述[J].控制理论与应用,1996,13(6):697-704.[XI Yugeng,CHAI Tianyou,HUI We-i min.Summary of genetic algorithms[J].Control Theory and Application,1996,13(6):697-704].
[10]林依华.综合全局寻优中遗传算法部分选择操作的研究[J].石油地球物理勘探,2000,35(5):641-650.[LI N Yihua.Re-search on partial selecting operation of genetic algorithm(GA)in integrative global opti mization[J].Oil Geophysical Prospecting,2000,35(5):641-650].
[11]王大力.用人工神经网络作地质数据分析[J].石油物探,1994,33(4):65-69.[WANG Dali.Geological data analysis u-sing artificial neural network[J].Geophysical Prospecting for Petroleum,1994,33(4):65-69].
[12]Anderson J K.Li mitations of seismic inversion for porosity and pore fluid:Lessons from chalk reservoir characterization exploration[C]//66th Ann.Internat.Mtg.,Soc.Expl.Geo-phys.,Expanded Abstracts.1996:309-312.
[13]Chi C Y,Mendel J M,Hampson D.Acomputationallyfast ap-proach to maxi mum-likelihood deconvolution[J].Geophysics,1984,49:550-565.
[14]Liu Z,Liu J.Seismic-controlled nonlinear extrapolation of well parameters using neural networks[J].Geophysics,1998,63:2035-2041.
[15]Schuelke J S,Quirein J A,Sarg J F.Reservoir architecture andporosity distribution,Pegasus field,West Texas-Anintegrat-ed sequence stratigraphy seismic attribute study using neural networks[C]//67th Ann.Internet.Mtg.,Soc.Expl.Geo-phys.,Expanded Abstracts.1997:668-671.
[16]Chen Q,Sidney S.Seismic attribute technology for reservoir forecasting and monitoring[J].The Leading Edge,1997,16:445-456.
[17]Daniel P Hampson,James S Schuelke,John A Quirein.Use of multi-attribute transforms to predict log properties fromseis-mic data[J].Geophysis,2001,29(6):220-236.
[18]Masters T.Signal and i mage processing with neural net-works:John Wiley&Sons,Inc.-1995,Advanced algorithms for neural networks:John Wiley&Sons,Inc.1994.
[19]Kalkomey C T.Potential risks when using seismic attributes as predictors of reservoir properties[J].The Leading Edge,1997,16:247-251.
[20]Leonard J A,Kramer M A,Ungar L H.Using radial basis functions to approxi mate a function and its error bounds[J].IEEE Trans.on Neural Networks,1992,3:624-627.
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