利用谱分解技术预测河流相储层
详细信息 本馆镜像全文    |  推荐本文 | | 获取馆网全文
摘要
利用谱分解技术对原始地震数据体进行相应的数学变换可得到调谐体、时频体和单频体(振幅体和相位体),进而通过对调谐体、时频体和单频体的解释得到对目标体的地质认识。本文应用最大熵法定量求取储层厚度,并对误差进行分析,同时运用频率扫描方法定性预测储层厚度变化,并利用广义S变换方法和交会融合预测沉积微相。首先,通过多井对比,分析测井相和砂体厚度;然后通过井震结合,分析各井对应井段的薄层砂岩振幅调谐体,确定合理的调谐频率,并对砂体厚度进行分析;再通过建立响应频率、砂体厚度与沉积微相之间的交会关系,在测井微相约束下预测沉积微相。应用结果证实,谱分解技术结合井资料可直观地反映河道砂体储层厚度分布和沉积微相区带展布规律。
Spectrum decomposition uses mathematical transformation to get tuning cube,time-frequency volume and single frequency volume(amplitude and phase)from seismic data.Maximum entropy method is applied in this article to calculate reservoir thickness,and the error is analyzed.At the same time the frequency scanning method is used to predict reservoir thickness and GST and RGB are used to predict sedimentary microfacies.First the log facies and sand body thickness are analyzed.Then the tuning amplitude of thin layer sandstones is used to determine reasonable tuning frequencies and analyze sand body thickness.Finally crossplots among response frequency,sand thickness,and sedimentary microfacies are built to predict the sedimentary microfacies under the restriction of well logging microfacies with loggingmicrofacies constrain.Application results confirm that the spectral decomposition combined with well data can intuitively reflect channel sand reservoir thickness and sedimentary microfacies belt distribution.
引文
[1]Partyka G,Gridley J,Lopez J.Interpretational applications of spectral decomposition in reservoir characterization.The Leading Edge,1999,18(1):353-3601.
    [2]Aydm A,Luis F C.Evolutionary chirp representation of non-stationary signals via Gabor transform.Signal Processing,2001,81(1):2429-2436.
    [3]Satish S H,Partha S R,Phil D A.Spectral decomposition of seismic data with continuous-wavelet transform.Geophysics,2005,70(6):19-251.
    [4]Stockwell R G,Mansin H L,Lowe R P.Localization of the complex spectrum:the S-transform.IEEE Transactions on Signal Processing,1996,44(4):998-1001.
    [5]Liu J.Spectral Decomposition and its Application in Mapping Stratigraphy and Hydrocarbons:Dissertation.Houston:University of Houston,2006.
    [6]张延庆,魏小东,王亚楠等.谱分解技术在QL油田小断层识别与解释中的应用.石油地球物理勘探,2006,41(5):584-591.Zhang Yanqing,Wei Xiaodong,Wang Yanan et al.Application of spectral factorization in recognition and interpretation of minor faults in QL oilfield.OGP,2006,41(5):584-591.
    [7]李小梅,俞丽娟.时频分析技术在层序旋回划分中的应用.石油与天然气地质,2008,29(6):793-796.Li Xiaomei,Yu Lijuan.Application of time-frequency analysis to division of cyclical sequence.Oil&Gas Geology,2008,29(6):793-796.
    [8]魏志平.谱分解调谐体技术在薄储层定量预测的应用.石油地球物理勘探,2009,44(3):337-340.Wei Zhiping.Application of spectrum-decomposition tuning body technique to quantitatively predict thin reservoir.OGP,2009,44(3):337-340.
    [9]刘建华,刘天放,李德春.薄层厚度定量解释研究.物探与化探,1997,21(1):23-28.Liu Jianhua,Liu Tianfang,Li Dechun.Quantitation interpretation of thin bed thickness.Geophysical and Geochemical Exploration,1997,21(1):23-28.
    [10]大港油田科技丛书编委会.大港油田开发实践.北京:石油工业出版社,1999,40-76.
    [11]大港油田石油地质志编辑委员会.中国石油地质志(卷四)——大港油田.北京:石油工业出版社,1991,300-328.
    [12]朱筱敏.沉积岩石学.北京:石油工业出版社,2008.
    [13]赵澄林等.胜利油田沉积储层与油气.北京:石油工业出版社,1999.
    [14]常文会,赵永刚,卢松.曲流河沉积微相与测井相特征分析.天然气工业,2010,30(2):48-51.Chang Wenhui,Zhao Yonggang,Lu Song.Features of sedimentary microfacies and electrofacies of meandering river deposits.Natural Gas Industry,2010,30(2):48-51.
    [15]丁次乾.矿场地球物理.山东东营:中国石油大学出版社,1992,4-18.
    [16]徐丽英,徐鸣洁,陈振岩.利用谱分解技术进行薄储层预测.石油地球物理勘探,2006,41(3):299-302.Xu Liying,Xu Mingjie,Chen Zhenyan.Using spectrum decomposition technique for prediction of thin reservoir.OGP,2006,42(3):299-302.
    [17]Bahorich M A,Motsch K L and Partyka G.Amplitude responses image reservoir.Hart's E&P,2002,January,59-61.
    [18]张宏.地震谱分解算法对比与局限性分析.勘探地球物理进展,2007,30(6):409-414.Zhang Hong.Comparison and limitations of seismic spectral decomposition algorithms.Progress in Exploration Geophysics,2007,30(6):409-414.

版权所有:© 2023 中国地质图书馆 中国地质调查局地学文献中心