测井多尺度分析方法及应用研究
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摘要
采用单条测井曲线进行地层评价存在着多解性和局限性。本文旨在利用测井多尺度分析方法,系统研究测井信号的多尺度特性和不同尺度上的测井信息融合,从原始测井信号中提取出地层的本质特征,建立测井数据向地质目标的映射关系,提高测井资料的可信度,以期将测井资料更好地用于地层评价。
根据小波的多尺度分析理论,在采用相关系数法选取最优小波基的基础上,系统研究单条测井曲线的多尺度分析方法;通过改变分析的尺度,实现了测井信号的多尺度边缘检测和多尺度滤波算法;针对测井多尺度系统,研究多条测井曲线的多尺度数据融合方法。在多类测井信号和融合信号多尺度分析的基础上,建立了测井多尺度估计方法,为层序地层和中子时间谱研究提供了理论基础。
针对多尺度分析的不同频率域,采用高频系数取极大值、低频系数分别基于平均法、加权法和边缘法的融合规则,依据多尺度分解和重构算法,系统研究同类测井信号和多类测井信号的数据融合。依据小波模极大重构算法,采用低频系数加权、高频系数取绝对值极大的融合规则,研究基于多尺度边缘检测的测井数据融合方法。采用熵和方差作为评判标准,统计数据表明融合曲线突出了测井曲线的公共信息,增加了地层信息量。
测井信号的多尺度分析将测井数据从一维深度域拓展为二维深度—尺度域,进而使其内部的能量聚集与分布得以清晰展示。依据小波时频色谱信息和能量信息,定量选取识别不同级别层序地层界面的最佳尺度;采用Morlet小波、高斯小波、正交小波应用于单条测井曲线,采用二次样条二进小波应用融合曲线,提取不同尺度下测井信号的时频特征,依据小波系数曲线的周期性振荡特征,与各级层序界面建立一定的对应关系,从而实现不同级别层序界面的划分;依据小波系数曲线的高频和低频响应识别出层序地层单元内部沉积特征。为地层层序的定量化研究提供了一种新思路。
针对PNN热中子时间谱,采用多尺度滤波算法,完成直观反映热中子衰减规律的色谱图。采用Morlet小波对PNN测井曲线以及融合曲线进行多尺度分析,依据小波能量信息可定性识别油水层。以多指数反演算法为基础,实现热中子寿命谱(τ谱)的反演。系统研究模拟数据的热中子寿命谱,分析地层水矿化度、孔隙度和饱和度对地层τ本征值的影响。数值模拟表明:热中子寿命谱的双峰分别反映井眼和地层对热中子的俘获能力;时间道的选取会影响τ谱是否呈现双峰和单峰结构。τ谱用于实际测井资料处理时可定性分辨油水层,由此可能引发寿命测井解释方法的改进和市场价值的提高。
Using a single log curve to evaluate strata leads to multi-solution and limitation. In order to overcome those aspects, multi-scale characters and the fusion of well logging data are studied systematically, based on the theory of multi-scale analysis, to extract the essential features of strata and to establish the corresponding relation between logging data and the strata, and therefore improving the reliability of log data applied to the evaluation of strata.
From the theory of multi-scale analysis, the multi-scale analysis of logging curve are studied systematically on the basis of the optimum wavelet base selected by means of correlation coefficient. With the scale variation, the algorithm of multi-scale edge detection and filtering of logging curve are constructed. The multi-scale data fusion are studied for well logging multi-scale system. Multi-scale analysis in well logging and its algorithm are developed through the multi-scale analysis of a single log curve and the fusion of multiple log curves. This method leads to the theory based on which sequence stratigraphy and neutron time spectrometry are studied.
For different frequency domains in multi-scale analysis, the fusion rules of logging data are discussed repectively, where high frequency coefficients choose maximal absolute values and low frequency coefficients are selectecd by virtue of average method, weighted method and edge method. Then combining the rules and multi-scale decomposition and reconstruction to study the fusion of one or more types of logging data. Finally based on the algorithm of reconstruction from modulus maxima of the wavelet transform and multi-scale edge detection, another fusion algorithm of logging data is built, where the maximal absolute values of high frequency and the weighted method of low frequency are selected. The results obtained by considering standard error and entropy to be evaluated criterion of the fusion data show the fusing curves enhance the common information of log curves and increase the information of strata.
By multi-scale analysis the logging data are transformed from one-dimension depth into two-dimension depth-scale domain, which makes the collection and distribution of its inner energy clearly revealed. Time-frequency chromatogram and energy information displayed by wavelet coefficients on different scales is used to determine the optimum scale corresponding to boundaries of stratigraphic sequence. When Morlet wavelet, Gauss wavelet and orthogonal wavelets are applied to a single log curve and 2-spline wavelet is used to the curve fused by multiple log curves, the corresponding relation between wavelet coeffients curves and the stratigraphic sequence is founded by detecting periodic components of logging data in multi-scale and observing the oscillating characteristic of wavelet coefficients, and therefore realizing the clarification of various rank stratigraphic sequence automatically. High and low frequency responses of wavelet coefficients revealing the change in sedimentary gyre are suitable to recognize sedimentary characteristics of the inner sequence stratum. All these researches provide a completely new method for the quantitative demarcating of sequence and its inner depositional features.
Based on multi-scale filtering algorithm, the chromatogram of thermal neutron counting efficiency is drawn from PNN logging instruments, which show the rule of neutron decay. Following the steps above, oil and water zone can be identified qualitatively. Time-frequency chromatogram and oscillating characteristics of wavelet coefficients are obtained from applications of Morlet wavelet to PNN logging curve and the fusion curve. Combining them to lead to clarify oil and water zone.
According to multi-exponential inversion algorithm, the inversion of thermal neutron life spectrum(τspectrum) is developed. Theτspectrum is studied by simulation data to analyze the effect of formation water salinity, porosity and oil saturation on the eigen value of thermal neutron life. Simulations results show that the double-peak ofτspectrum reveal the ability of the wellbore and the formation to capture thermal neutron; and the appearance of single-peak or double-peak forτspectrum has bearing on the time chose. The research shows that the application ofτspectrum to actual logging data may identify oil and water zone. Therefore this method may ameliorate log interpretation of neutron life and improve market value.
根据小波的多尺度分析理论,在采用相关系数法选取最优小波基的基础上,系统研究单条测井曲线的多尺度分析方法;通过改变分析的尺度,实现了测井信号的多尺度边缘检测和多尺度滤波算法;针对测井多尺度系统,研究多条测井曲线的多尺度数据融合方法。在多类测井信号和融合信号多尺度分析的基础上,建立了测井多尺度估计方法,为层序地层和中子时间谱研究提供了理论基础。
针对多尺度分析的不同频率域,采用高频系数取极大值、低频系数分别基于平均法、加权法和边缘法的融合规则,依据多尺度分解和重构算法,系统研究同类测井信号和多类测井信号的数据融合。依据小波模极大重构算法,采用低频系数加权、高频系数取绝对值极大的融合规则,研究基于多尺度边缘检测的测井数据融合方法。采用熵和方差作为评判标准,统计数据表明融合曲线突出了测井曲线的公共信息,增加了地层信息量。
测井信号的多尺度分析将测井数据从一维深度域拓展为二维深度—尺度域,进而使其内部的能量聚集与分布得以清晰展示。依据小波时频色谱信息和能量信息,定量选取识别不同级别层序地层界面的最佳尺度;采用Morlet小波、高斯小波、正交小波应用于单条测井曲线,采用二次样条二进小波应用融合曲线,提取不同尺度下测井信号的时频特征,依据小波系数曲线的周期性振荡特征,与各级层序界面建立一定的对应关系,从而实现不同级别层序界面的划分;依据小波系数曲线的高频和低频响应识别出层序地层单元内部沉积特征。为地层层序的定量化研究提供了一种新思路。
针对PNN热中子时间谱,采用多尺度滤波算法,完成直观反映热中子衰减规律的色谱图。采用Morlet小波对PNN测井曲线以及融合曲线进行多尺度分析,依据小波能量信息可定性识别油水层。以多指数反演算法为基础,实现热中子寿命谱(τ谱)的反演。系统研究模拟数据的热中子寿命谱,分析地层水矿化度、孔隙度和饱和度对地层τ本征值的影响。数值模拟表明:热中子寿命谱的双峰分别反映井眼和地层对热中子的俘获能力;时间道的选取会影响τ谱是否呈现双峰和单峰结构。τ谱用于实际测井资料处理时可定性分辨油水层,由此可能引发寿命测井解释方法的改进和市场价值的提高。
Using a single log curve to evaluate strata leads to multi-solution and limitation. In order to overcome those aspects, multi-scale characters and the fusion of well logging data are studied systematically, based on the theory of multi-scale analysis, to extract the essential features of strata and to establish the corresponding relation between logging data and the strata, and therefore improving the reliability of log data applied to the evaluation of strata.
From the theory of multi-scale analysis, the multi-scale analysis of logging curve are studied systematically on the basis of the optimum wavelet base selected by means of correlation coefficient. With the scale variation, the algorithm of multi-scale edge detection and filtering of logging curve are constructed. The multi-scale data fusion are studied for well logging multi-scale system. Multi-scale analysis in well logging and its algorithm are developed through the multi-scale analysis of a single log curve and the fusion of multiple log curves. This method leads to the theory based on which sequence stratigraphy and neutron time spectrometry are studied.
For different frequency domains in multi-scale analysis, the fusion rules of logging data are discussed repectively, where high frequency coefficients choose maximal absolute values and low frequency coefficients are selectecd by virtue of average method, weighted method and edge method. Then combining the rules and multi-scale decomposition and reconstruction to study the fusion of one or more types of logging data. Finally based on the algorithm of reconstruction from modulus maxima of the wavelet transform and multi-scale edge detection, another fusion algorithm of logging data is built, where the maximal absolute values of high frequency and the weighted method of low frequency are selected. The results obtained by considering standard error and entropy to be evaluated criterion of the fusion data show the fusing curves enhance the common information of log curves and increase the information of strata.
By multi-scale analysis the logging data are transformed from one-dimension depth into two-dimension depth-scale domain, which makes the collection and distribution of its inner energy clearly revealed. Time-frequency chromatogram and energy information displayed by wavelet coefficients on different scales is used to determine the optimum scale corresponding to boundaries of stratigraphic sequence. When Morlet wavelet, Gauss wavelet and orthogonal wavelets are applied to a single log curve and 2-spline wavelet is used to the curve fused by multiple log curves, the corresponding relation between wavelet coeffients curves and the stratigraphic sequence is founded by detecting periodic components of logging data in multi-scale and observing the oscillating characteristic of wavelet coefficients, and therefore realizing the clarification of various rank stratigraphic sequence automatically. High and low frequency responses of wavelet coefficients revealing the change in sedimentary gyre are suitable to recognize sedimentary characteristics of the inner sequence stratum. All these researches provide a completely new method for the quantitative demarcating of sequence and its inner depositional features.
Based on multi-scale filtering algorithm, the chromatogram of thermal neutron counting efficiency is drawn from PNN logging instruments, which show the rule of neutron decay. Following the steps above, oil and water zone can be identified qualitatively. Time-frequency chromatogram and oscillating characteristics of wavelet coefficients are obtained from applications of Morlet wavelet to PNN logging curve and the fusion curve. Combining them to lead to clarify oil and water zone.
According to multi-exponential inversion algorithm, the inversion of thermal neutron life spectrum(τspectrum) is developed. Theτspectrum is studied by simulation data to analyze the effect of formation water salinity, porosity and oil saturation on the eigen value of thermal neutron life. Simulations results show that the double-peak ofτspectrum reveal the ability of the wellbore and the formation to capture thermal neutron; and the appearance of single-peak or double-peak forτspectrum has bearing on the time chose. The research shows that the application ofτspectrum to actual logging data may identify oil and water zone. Therefore this method may ameliorate log interpretation of neutron life and improve market value.
引文
1. 文成林,周东华.多尺度估计理论及其应用[M].北京:清华大学出版社,2002:1-125
2. 文成林.多尺度估计理论及方法研究[D].西安:西北工业大学博士论文,1999
3. Saksena V R, Reilly J O, Kokotovic P V. Singular perturbations and time-scale methods in control theory [J]:Survey 1976-1983. Automatica,20(3):273-293
4. Hong L. Multiresolutional distributed Filtering [J].IEEE Trans. on Automatic Control, 1994,39(4):1244~1253
5. Daubechies I. Ten lectures on wavelet [M]. Philadephia,Pennsylvania:Society for Industrial and Appl. Math.,1992
6. Hong L. Multiresolutional multiple-model target tracking [J]. IEEE Trans. on Aerospace and Electronic Systems,1993,30(2):518~524
7. Kadambe S.,D. F. Boudreaux-Bartels. Applications of the wavelet transform for pitch detection of speech signals [J]. IEEE Trans. On Information Theory,1992,38(2):917-924
8. 段虞荣,郑继明,段绍光.小波分析在油气田地球物理勘探中的应用[J].重庆大学学报(自然科学版),1996,19(6):44-53
9. 段虞荣,颜新祥,郑继明.运用多重分形和小波变换预测油气储量及确定勘探井位[J]. 应用数学学报,1997,12A(1):99-106
10. 何光明,高如曾,韩德贵,等.小波变换在地震资料高分辨处理中的应用[J].石油物探,1996,35(2):44-54
11. Mallat S G. Multiresolutional approximation and wavelets [J].Dept. Of Computer and Infor. Science. U.Of Penn.,Ms-GIS-87-87,GRASP Lab.80,1987
12. Chou K C.A stochastic modeling approach to multiscale signal processing MIT [D]. Dept. Of Electtrical Engineering and Computer Science, Ph. D Thesis,1990
13. Mallat S G. A compact multiresolution representation:the wavelet model[J].Dept. Of Computer and Info. Science. U. Of Penn.,Ms-CIS-87-22,GRASP LAB 113,Aug 1987
14. Benveniste A,Nikoukhah R,Willsky A S. Multiscale system theory [R]. In:Proc. 29th IEEE Conference on Decision and Control, Honolulu,HI,1990,2484-2487
15. Basseville M, Benveniste A, Chou K C,et al. Modeling and estimation of multiresolution stochastic processes[J]. IEEE Trans. on Information Theory,1992,38(2):766-784
16. Chou K C, Willsky A S, Benveniste A. Multiscale recursive estimation, data Fusion, and regularization[J]. IEEE Trans. on Automatic Control,1994,39(3):464-478
17. 绍远,何发昌,彭健.一种机器人非视觉多传感器信息融合方法[J].电子学报,1996,24(8):94-97
18. Basseville M, Benveniste A, Willsky A S. Multiscale autoregressive process[J], Parts I:Schur-Levinson Parametrization. IEEE Trans. on Signal Processing,1992,40(8):1915-1934
19. Basseville M, Benveniste A, Willsky A S. Multiscale autoregressive process, Parts Ⅱ,Lattice Structures for Whitening and Modeling[J]. IEEE Trans. on Signal Processing,1992,40(8):1935-1953
20. Willsky A S. A multiscale approach to sensor fusion and the solution of linear inverse problems [J]. Applied Computational Harmonic Analysis,1995,2:127-147
21. Hong L. Distributed filtering using set models [J].IEEE Trans. on Aerospace and Electronic Systems,1992,28(4):1144-1153
22. 赵巍,潘泉,戴冠中,等.多尺度数据融合算法概述[J].系统工程与电子技术,2001,23(6):66-69
23. 赵巍.多尺度系统建模、估计与融合方法研究[D].西安:西北工业大学博士论文, 2001
24. 文成林.多传感器单模型动态系统多尺度数据融合算法[J].电子学报,2001,29(3):341-345
25. 文成林,金峰,周东华.单传感器单模型动态系统多尺度分解与估计新算法[J].电子学报,2002,30(6):819-822
26. 闫莉萍.多尺度数据融合状态估计算法研究[D].河南:河南大学硕士论文,2003
27. 何友,王国宏, 等.多传感器信息融合及应用[M].北京:电子工业出版社,2000
28. Linas J, Waltz E.Multisensor data fusion[M].Artech House, Norwood, Massachusetts, 1990
29. Varshney, P.K. Multisensor data fusion[J].Electronics & CommunicationEngineering Journal,1997,9(6):245-253
30. Lakin W L, Miles J A H. IKBS in multisensor data fusions [J].Proc. IEEE Conference on Advances in C I,Publication 247,1985:234-240
31. 何 友 , 彭 应 宁 , 等 . 多 传 感 器 数 据 融 合 系 统 中 两 种 新 的 航 迹 相 关 算 法 [J]. 电 子 学报,1997,25(9):10-19
32. 孙红岩,毛士艺.多传感器目标识别的数据融合[J].电子学报,1995,23(10):188-193
33. 杨福生.小波变换的工程分析与应用[M].北京:科学出版社,1999
34. 徐 辰,赵瑞珍,甘小冰. 小波分析·应用算法[M]. 北京:科学出版,2004
35. 孙延奎.小波分析及其应用[M].北京:机械工业出版社, 2005,159-202
36. 彭玉华.小波变换与工程应用[M].北京:科学出版社,1999
37. 王才经.现代应用数学[M].东营:石油大学出版社,2004
38. Mark H. Scheihing. Multiscale Reservoir Description Models for PerformancePrediction in the Kuparuk River Field [J],North Slope of Alaska. SPE 76753,2002
39. Sang Heon Lee, et al.Multiscale Data Integration Using Markov Random Fields[J]. SPE 76905,2002,68-78
40. Scott R Reeves, et al.Feasibility Assessment of a New Approach for integratingMultiscale Data for High Resolution Reservoir Characterization[J]. SPE 77759,2002
41. Paul F Worthington.The Effect of Scale on the Petrophysical Estimation of Intergranular Permeability[J].SPWLA 44th Annual Logging Symposium, 2003, June, 22 -25
42. Adel Malallah, et al. Multiscale Integration Using Markov Random Fields and Markov Chain Monte Carlo: A Field Application in the Middle-East[J]. SPE 81544,2003,
43. 刘亚东,龚育昌,宋玉凤,等.数据融合在注入剖面组合测井综合解释中的应用[J].测井技术, 2002, 26(5),406-408
44. 王永刚.综合参数法再地球物理资料处理和分析中的应用[J].石油物探,1997,36(9):102-106
45. 万应明,高峻,董建平,等.多测井曲线的综合处理合成[J].石油地球物理勘探.2005,40(2):243-247
46. 赵瑞珍,徐 龙,宋国乡.基于小波变换的图像多尺度数据融合[J].计算机辅助设计与图形学学报,2002,14(4):361-364
47. 万应明,高 峻,董建平,等.多测井曲线合成应用方法初探[J].石油探勘,2005,44(1):71-75
48. 杨惠娟,张建秋.基于小波去噪和数据融合的多传感器数据重建算法[J] .复旦学报(自然科学版),2005,44(1):161-165
49. 董毓敏.基于小波叠加的主成分变换遥感数据融合方法的研究[J].东北测绘,2002,25(3):10-17
50. 焦翠华,李冰.零通小波用于测井曲线多尺度分层[J].测井技术,1999,23(3):173-175
51. 孙卫涛,等.基于多尺度分析的正交偶极子声波测井反演地层各向异性[J].石油大学学报,2003,27(1):23-28
52. 付正文,邹长春,尉中良,等.利用小波变换检测超声成象测井图象边缘的研究[J].现代地质, 2002,16(3):322-326
53. 黄捍东,等.测井约束多尺度储层厚度反演[J].成都理工学院学报,1999,26(4):343-347
54. Vail,P.R.等编,徐怀大等译.层序地层学原理:海平面变化综合分析[M].北京:石油工业出版社,1996
55. Vail.P.R, et al.Seismic Stratigraphy and Global Changes of Sea Level[M].In: Seismic Stratigraphy Applications to Hydrocarbon Exploration, Am.Assoc Petrol.Geol.Mem. 1977,26,49-212
56. Vail.P.R, et al.The Stratigraphic Signatures or Tectonics,Eustasy and Sedimentology-an Overview[M]. In: Cycles and Events In Stratigraphy, New York: Springer Verlag,1987
57. 姜在兴,李华启,王留奇,等.层序地层学原理及应用[M].北京:石油工业出版社,1996
58. 纪友亮,张世奇.层序地层学原理及层序成因机制模式[M].北京:地质出版社,1998,1-89
59. 朱筱敏.层序地层学[M].东营:石油大学出版社,2000
60. 邓宏文,等.高分辨率层序地层学:原理及应用[M].北京:地质出版社,2002
61. 蔡希源,宋国奇,等.陆相盆地高精度层序地层学—隐蔽油气藏勘探基础、方法与实践[M].北京:地质出版社,2004
62. Embry.A.F. Sequence Boundaries and Sequency Hierarchies:Problems and Proposals[M]. In:Sequency Stratigraphy on the Northwest European Margin.Amsterdam:Elsevier,1995
63. Wang Hong-zhen.A Scheme of the Hierarchy for Sequence Stratigraphy[J]. Journal of China University of Grosciences, 1996,7(1):1-12
64. 王贵文,郭荣坤.测井地质学[M].北京:石油工业出版社,2000,75-78.
65. 黄隆基. 核测井原理:光子-中子-核磁[M].东营:石油大学出版社,2000
66. 丁次乾.矿场地球物理[M].东营:石油大学出版社,1992
67. 张占松.测井资料识别层序的方法及问题探讨[J].石油勘探与开发,2000,27(5):119-121
68. 姜在兴,李华启,王留奇,等.层序地层学原理及应用[M].北京:石油工业出版社,1996.
69. 操应长,姜在兴,夏斌,等.利用测井资料识别层序地层界面的几种方法[J].石油大学学报,2003,27(2):23-26
70. 余继峰,李增学.测井数据小波变换及其地质意义[J].中国矿业大学学报,2003,32(3):336-339
71. 邵才瑞 , 李 洪奇 , 张福 明 , 等 . 用 测 井 曲 线 自 动 划 分 层 序 地 层 研 究 [J]. 地 层学杂 志 , 2004,28(4):321-325
72. 陈茂山.测井资料的两种深度域频谱分析方法及在层序地层学研究中的应用[J].石油地球物理勘探, 1999,34(1):57-64
73. 朱剑兵,赵培坤.利用测井曲线活度划分准层序[J].新疆石油地质, 2005,26(4):426-427
74. 王贵文,邓清平,唐为清.测井曲线谱分析方法及在沉积旋回研究中的应用[J].石油勘探与开发, 2002,29(1):93-95.
75. 宗孔德,胡广书.数字信号处理[M].北京:清华大学出版社,1986,37-143.
76. 房文静,范宜仁,邓少贵,等.测井多尺度分析方法中最优小波基的选取[J].煤田地质与勘探, 2006,34(4):71-73
77. Mallat S G, Zhong S F.Characterization of signals form multiscale edges[J]. IEEE Trans. on PAMI, 1992,14(7):710-732.
78. 李凤杰,王多云,郑希民,等.测井曲线频谱分析在含煤地层沉积旋回研究中的应用[J].煤田地质与勘探,2003,31(6):14-18.
79. 陈正星.小波分析算法与应用[M].西安:西安交通大学出版社,1998.
80. Farge M. Wavelet transforms and their application to turbulence[J].Annu. Rev. Fluid Mech,1992,24,395-457.
81. Mallat S G. Multiresolution approximation and wavelets[J].Trans. Amer. Math.Soc.1989,315:69-88
82. 史晓峰,李 静,易京兵.应用小波分析改善随钻电阻率测量的纵向分辨率[J].西安石油学院学报,2002,17(6):28-31
83. 冯国庆,杨琨,陈忠强,等.提高测井曲线纵向分辨率的小波变换计数[J] .新疆石油地质,2004,25(5):529-531
84. Mallat S G, Zhong S. Characterization of Signals form Multiscale Edges [J].IEEE Trans PAMI, 1992,14(7):710-732
85. Mallat S G, Hwang W-L. Singularity detection and processing with wavelets[J].IEEE Transactionsons on information Theory, 1992,38:617-642
86. 周小勇,叶银忠.故障信号的小波基选择方法[J].控制工程,2003,10(4):308-311
87. 陈希平,毛海杰,李炜.基于 MATLAB 的奇异姓检测中的小波基选取研究[J].计算机仿真,2004,21(11):48-50
88. 刘素美,李书光.超声检测信号处理的小波基选取[J].无损探伤,2004,28(6):12-15
89. 康中蔚,罗飞路,潘孟春,等.小波奇异性检测理论在交变磁场法缺陷定量中的应用[J].无损检测,2005,27(7):359-363
90. 杨宗凯.小波去噪及其在信号检测中的应用[J]. 华中理工大学学报,1997,25(2):1-4
91. 胡昌华,张军波,夏军,等.基于 MATLAB 的系统分析与设计—小波分析[M].西安:电子科技大学出版社,1999
92. 冯林,成璐,吴振宇.基于提升算法的测井信号去噪处理[J] .石油物探,2004,43(3):238-241
93. 乔强,周激流,何坤等.基于小波变换的非平稳信号去噪[J].计算机应用研究,2005,(8):161-163
94. 潘泉,张磊,孟晋丽,等.小波滤波方法及应用[J].北京:清华大学出版社,2005
95. 郑继明.正交样条小波的指数衰减性[J].重庆邮电学院学报,2000,12(1):11-13
96. Dahmen W, Kunoth A, Urban K. Biorthogonal alpine wavelet on the interval-stability and moment conditions[J].Apllied and computa- tional harmonic analysis,1996(6): 132-296
97. 吴正毅.测试技术与测试信号处理[M].北京:清华大学出版社,1991
98. Petrou M, Stassopoulou A. Advanced techniques for fusion of information in remote sensing: an overview [A].Proc. SPIE[C], Florence, Italy, 1999,3871:264- 275
99. Bruno Aiazzi, Luciano Alparone, Fabrizio Argenti. Wavelet and pyramid techniques for multisensor data fusion: a performance comparion varying with scale ratios[A].Proc. SPIE[C], Florence, Italy, 1999,3871:251- 262
100. 晁锐,张科,李言俊.一种基于小波变换的图像融合算法[J] .电子学报,2004,32(5): 750-753
101. 林卉,杜培军,张莲蓬.基于小波变换的遥感影像融合与评价[J].煤炭学报,2005,30(3):332-336
102. 陈木生,狄红卫.多聚焦图像融合的最佳小波分解层研究[J] .光电工程,2004,31(3): 64-67
103. 雍世和,张超谟.测井数据处理与综合解释[M].山东东营:石油大学出版社,1996
104. 谭廷栋.测井学[M].北京:石油工业出版社,1998
105. 张庚骥.电法测井(上册)[M].北京:石油工业出版社,1984
106. 曾文冲.油气藏储集层测井评价技术[M].北京:石油工业出版社,1991
107. 王卫红,姜在兴,操应长,等.测井曲线识别层序边界的方法探讨[J].西南石油学院学报,2003,25(3):1-4
108. 郑小武,邓宏文,徐怀大等.测井高分辨率层序地层自动划分技术与应用尝试[J].石油与天然气地质.1999,20(4):357-360
109.李庆谋,刘少华.地球物理测井序列的小波波谱方法[J].地球物理学进展, 2002,17(12):78-83
110.李江涛,余继峰,李增学.基于测井数据小波变换的层序划分[J].煤田地质与勘探, 2004,32(2):48-50
111.Pati Y C, Krishnaprasad P S. Analysis an Synthesis of Feedforward Neural Networks Using Discrete Affine Wavelet Transformations[J].IEEE Trans on Neural Networks,1993,4(1):73-85
112.李全厚,李汉川,穆晓雁,等.新一代套管井储层评价技术—脉冲中子中子(PNN)测井[J] .国外测井技术,2004,19(4):11-15
113.洪有密.测井原理与综合解释[M]. 东营:石油大学出版社,2002
114. 任晓荣.核测井信号数字滤波器的设计与实现[J].传感器世界,2003,(6):11-16
115. 任晓荣,赵福宇.核测井信号数字滤波器的性能分析[J].核电子学与探测技术,2005,25(6):742-745
116. 王瑞平.岩石样品 NMR 驰豫信号的数学处理[J].测井技术,1997,26(6):397-400
117. Dines K, Lyttle J.Computerized geophysical tomography[J]. Proc IEEE,1979,67:1065-1073
118. 王为民,李培,叶朝辉.核磁共振驰豫信号的多指数反演[J] .中国科学(A 辑),2001,31(8):730-736
119. 翁爱华,李舟波,莫修文,等.核磁共振测井数据高分辨率反演方法研究[J] .测井技术,2002,26(6):455-459
120. 刘堂晏,肖立志,傅容珊,等.球管孔隙模型的核磁共振(NMR)驰豫特征及应用[J].地球物理学报,2004,47(4):663-671
121. 童茂松,李莉,王伟男,等.岩石激发极化驰豫时间谱与孔隙结构、渗透率的关系[J] .地球物理学报,2005,48(3):710-716
122. 肖立志.核磁共振成像测井与岩石核磁共振及其应用[M].北京:科学出版社,1998
123. 肖立志,石红兵.低场核磁共振岩心分析及其在测井解释中的应用[J] .测井技术,1998,22(1):42-49
124. 姚绪刚,王忠东.一种新的核磁共振驰豫谱反演算法[J] .测井技术,2003,27(5):373-376
125. 王丽丽,张一鸣,陈继忠.低场脉冲横向驰豫信号的反演分析[J].分析仪器,2005,(4):8-11
126. 王忠东,肖立志,刘堂宴.核磁共振驰豫信号多指数反演新方法及其应用[J].中国科学(G 辑),2003,33(4):323-331
2. 文成林.多尺度估计理论及方法研究[D].西安:西北工业大学博士论文,1999
3. Saksena V R, Reilly J O, Kokotovic P V. Singular perturbations and time-scale methods in control theory [J]:Survey 1976-1983. Automatica,20(3):273-293
4. Hong L. Multiresolutional distributed Filtering [J].IEEE Trans. on Automatic Control, 1994,39(4):1244~1253
5. Daubechies I. Ten lectures on wavelet [M]. Philadephia,Pennsylvania:Society for Industrial and Appl. Math.,1992
6. Hong L. Multiresolutional multiple-model target tracking [J]. IEEE Trans. on Aerospace and Electronic Systems,1993,30(2):518~524
7. Kadambe S.,D. F. Boudreaux-Bartels. Applications of the wavelet transform for pitch detection of speech signals [J]. IEEE Trans. On Information Theory,1992,38(2):917-924
8. 段虞荣,郑继明,段绍光.小波分析在油气田地球物理勘探中的应用[J].重庆大学学报(自然科学版),1996,19(6):44-53
9. 段虞荣,颜新祥,郑继明.运用多重分形和小波变换预测油气储量及确定勘探井位[J]. 应用数学学报,1997,12A(1):99-106
10. 何光明,高如曾,韩德贵,等.小波变换在地震资料高分辨处理中的应用[J].石油物探,1996,35(2):44-54
11. Mallat S G. Multiresolutional approximation and wavelets [J].Dept. Of Computer and Infor. Science. U.Of Penn.,Ms-GIS-87-87,GRASP Lab.80,1987
12. Chou K C.A stochastic modeling approach to multiscale signal processing MIT [D]. Dept. Of Electtrical Engineering and Computer Science, Ph. D Thesis,1990
13. Mallat S G. A compact multiresolution representation:the wavelet model[J].Dept. Of Computer and Info. Science. U. Of Penn.,Ms-CIS-87-22,GRASP LAB 113,Aug 1987
14. Benveniste A,Nikoukhah R,Willsky A S. Multiscale system theory [R]. In:Proc. 29th IEEE Conference on Decision and Control, Honolulu,HI,1990,2484-2487
15. Basseville M, Benveniste A, Chou K C,et al. Modeling and estimation of multiresolution stochastic processes[J]. IEEE Trans. on Information Theory,1992,38(2):766-784
16. Chou K C, Willsky A S, Benveniste A. Multiscale recursive estimation, data Fusion, and regularization[J]. IEEE Trans. on Automatic Control,1994,39(3):464-478
17. 绍远,何发昌,彭健.一种机器人非视觉多传感器信息融合方法[J].电子学报,1996,24(8):94-97
18. Basseville M, Benveniste A, Willsky A S. Multiscale autoregressive process[J], Parts I:Schur-Levinson Parametrization. IEEE Trans. on Signal Processing,1992,40(8):1915-1934
19. Basseville M, Benveniste A, Willsky A S. Multiscale autoregressive process, Parts Ⅱ,Lattice Structures for Whitening and Modeling[J]. IEEE Trans. on Signal Processing,1992,40(8):1935-1953
20. Willsky A S. A multiscale approach to sensor fusion and the solution of linear inverse problems [J]. Applied Computational Harmonic Analysis,1995,2:127-147
21. Hong L. Distributed filtering using set models [J].IEEE Trans. on Aerospace and Electronic Systems,1992,28(4):1144-1153
22. 赵巍,潘泉,戴冠中,等.多尺度数据融合算法概述[J].系统工程与电子技术,2001,23(6):66-69
23. 赵巍.多尺度系统建模、估计与融合方法研究[D].西安:西北工业大学博士论文, 2001
24. 文成林.多传感器单模型动态系统多尺度数据融合算法[J].电子学报,2001,29(3):341-345
25. 文成林,金峰,周东华.单传感器单模型动态系统多尺度分解与估计新算法[J].电子学报,2002,30(6):819-822
26. 闫莉萍.多尺度数据融合状态估计算法研究[D].河南:河南大学硕士论文,2003
27. 何友,王国宏, 等.多传感器信息融合及应用[M].北京:电子工业出版社,2000
28. Linas J, Waltz E.Multisensor data fusion[M].Artech House, Norwood, Massachusetts, 1990
29. Varshney, P.K. Multisensor data fusion[J].Electronics & CommunicationEngineering Journal,1997,9(6):245-253
30. Lakin W L, Miles J A H. IKBS in multisensor data fusions [J].Proc. IEEE Conference on Advances in C I,Publication 247,1985:234-240
31. 何 友 , 彭 应 宁 , 等 . 多 传 感 器 数 据 融 合 系 统 中 两 种 新 的 航 迹 相 关 算 法 [J]. 电 子 学报,1997,25(9):10-19
32. 孙红岩,毛士艺.多传感器目标识别的数据融合[J].电子学报,1995,23(10):188-193
33. 杨福生.小波变换的工程分析与应用[M].北京:科学出版社,1999
34. 徐 辰,赵瑞珍,甘小冰. 小波分析·应用算法[M]. 北京:科学出版,2004
35. 孙延奎.小波分析及其应用[M].北京:机械工业出版社, 2005,159-202
36. 彭玉华.小波变换与工程应用[M].北京:科学出版社,1999
37. 王才经.现代应用数学[M].东营:石油大学出版社,2004
38. Mark H. Scheihing. Multiscale Reservoir Description Models for PerformancePrediction in the Kuparuk River Field [J],North Slope of Alaska. SPE 76753,2002
39. Sang Heon Lee, et al.Multiscale Data Integration Using Markov Random Fields[J]. SPE 76905,2002,68-78
40. Scott R Reeves, et al.Feasibility Assessment of a New Approach for integratingMultiscale Data for High Resolution Reservoir Characterization[J]. SPE 77759,2002
41. Paul F Worthington.The Effect of Scale on the Petrophysical Estimation of Intergranular Permeability[J].SPWLA 44th Annual Logging Symposium, 2003, June, 22 -25
42. Adel Malallah, et al. Multiscale Integration Using Markov Random Fields and Markov Chain Monte Carlo: A Field Application in the Middle-East[J]. SPE 81544,2003,
43. 刘亚东,龚育昌,宋玉凤,等.数据融合在注入剖面组合测井综合解释中的应用[J].测井技术, 2002, 26(5),406-408
44. 王永刚.综合参数法再地球物理资料处理和分析中的应用[J].石油物探,1997,36(9):102-106
45. 万应明,高峻,董建平,等.多测井曲线的综合处理合成[J].石油地球物理勘探.2005,40(2):243-247
46. 赵瑞珍,徐 龙,宋国乡.基于小波变换的图像多尺度数据融合[J].计算机辅助设计与图形学学报,2002,14(4):361-364
47. 万应明,高 峻,董建平,等.多测井曲线合成应用方法初探[J].石油探勘,2005,44(1):71-75
48. 杨惠娟,张建秋.基于小波去噪和数据融合的多传感器数据重建算法[J] .复旦学报(自然科学版),2005,44(1):161-165
49. 董毓敏.基于小波叠加的主成分变换遥感数据融合方法的研究[J].东北测绘,2002,25(3):10-17
50. 焦翠华,李冰.零通小波用于测井曲线多尺度分层[J].测井技术,1999,23(3):173-175
51. 孙卫涛,等.基于多尺度分析的正交偶极子声波测井反演地层各向异性[J].石油大学学报,2003,27(1):23-28
52. 付正文,邹长春,尉中良,等.利用小波变换检测超声成象测井图象边缘的研究[J].现代地质, 2002,16(3):322-326
53. 黄捍东,等.测井约束多尺度储层厚度反演[J].成都理工学院学报,1999,26(4):343-347
54. Vail,P.R.等编,徐怀大等译.层序地层学原理:海平面变化综合分析[M].北京:石油工业出版社,1996
55. Vail.P.R, et al.Seismic Stratigraphy and Global Changes of Sea Level[M].In: Seismic Stratigraphy Applications to Hydrocarbon Exploration, Am.Assoc Petrol.Geol.Mem. 1977,26,49-212
56. Vail.P.R, et al.The Stratigraphic Signatures or Tectonics,Eustasy and Sedimentology-an Overview[M]. In: Cycles and Events In Stratigraphy, New York: Springer Verlag,1987
57. 姜在兴,李华启,王留奇,等.层序地层学原理及应用[M].北京:石油工业出版社,1996
58. 纪友亮,张世奇.层序地层学原理及层序成因机制模式[M].北京:地质出版社,1998,1-89
59. 朱筱敏.层序地层学[M].东营:石油大学出版社,2000
60. 邓宏文,等.高分辨率层序地层学:原理及应用[M].北京:地质出版社,2002
61. 蔡希源,宋国奇,等.陆相盆地高精度层序地层学—隐蔽油气藏勘探基础、方法与实践[M].北京:地质出版社,2004
62. Embry.A.F. Sequence Boundaries and Sequency Hierarchies:Problems and Proposals[M]. In:Sequency Stratigraphy on the Northwest European Margin.Amsterdam:Elsevier,1995
63. Wang Hong-zhen.A Scheme of the Hierarchy for Sequence Stratigraphy[J]. Journal of China University of Grosciences, 1996,7(1):1-12
64. 王贵文,郭荣坤.测井地质学[M].北京:石油工业出版社,2000,75-78.
65. 黄隆基. 核测井原理:光子-中子-核磁[M].东营:石油大学出版社,2000
66. 丁次乾.矿场地球物理[M].东营:石油大学出版社,1992
67. 张占松.测井资料识别层序的方法及问题探讨[J].石油勘探与开发,2000,27(5):119-121
68. 姜在兴,李华启,王留奇,等.层序地层学原理及应用[M].北京:石油工业出版社,1996.
69. 操应长,姜在兴,夏斌,等.利用测井资料识别层序地层界面的几种方法[J].石油大学学报,2003,27(2):23-26
70. 余继峰,李增学.测井数据小波变换及其地质意义[J].中国矿业大学学报,2003,32(3):336-339
71. 邵才瑞 , 李 洪奇 , 张福 明 , 等 . 用 测 井 曲 线 自 动 划 分 层 序 地 层 研 究 [J]. 地 层学杂 志 , 2004,28(4):321-325
72. 陈茂山.测井资料的两种深度域频谱分析方法及在层序地层学研究中的应用[J].石油地球物理勘探, 1999,34(1):57-64
73. 朱剑兵,赵培坤.利用测井曲线活度划分准层序[J].新疆石油地质, 2005,26(4):426-427
74. 王贵文,邓清平,唐为清.测井曲线谱分析方法及在沉积旋回研究中的应用[J].石油勘探与开发, 2002,29(1):93-95.
75. 宗孔德,胡广书.数字信号处理[M].北京:清华大学出版社,1986,37-143.
76. 房文静,范宜仁,邓少贵,等.测井多尺度分析方法中最优小波基的选取[J].煤田地质与勘探, 2006,34(4):71-73
77. Mallat S G, Zhong S F.Characterization of signals form multiscale edges[J]. IEEE Trans. on PAMI, 1992,14(7):710-732.
78. 李凤杰,王多云,郑希民,等.测井曲线频谱分析在含煤地层沉积旋回研究中的应用[J].煤田地质与勘探,2003,31(6):14-18.
79. 陈正星.小波分析算法与应用[M].西安:西安交通大学出版社,1998.
80. Farge M. Wavelet transforms and their application to turbulence[J].Annu. Rev. Fluid Mech,1992,24,395-457.
81. Mallat S G. Multiresolution approximation and wavelets[J].Trans. Amer. Math.Soc.1989,315:69-88
82. 史晓峰,李 静,易京兵.应用小波分析改善随钻电阻率测量的纵向分辨率[J].西安石油学院学报,2002,17(6):28-31
83. 冯国庆,杨琨,陈忠强,等.提高测井曲线纵向分辨率的小波变换计数[J] .新疆石油地质,2004,25(5):529-531
84. Mallat S G, Zhong S. Characterization of Signals form Multiscale Edges [J].IEEE Trans PAMI, 1992,14(7):710-732
85. Mallat S G, Hwang W-L. Singularity detection and processing with wavelets[J].IEEE Transactionsons on information Theory, 1992,38:617-642
86. 周小勇,叶银忠.故障信号的小波基选择方法[J].控制工程,2003,10(4):308-311
87. 陈希平,毛海杰,李炜.基于 MATLAB 的奇异姓检测中的小波基选取研究[J].计算机仿真,2004,21(11):48-50
88. 刘素美,李书光.超声检测信号处理的小波基选取[J].无损探伤,2004,28(6):12-15
89. 康中蔚,罗飞路,潘孟春,等.小波奇异性检测理论在交变磁场法缺陷定量中的应用[J].无损检测,2005,27(7):359-363
90. 杨宗凯.小波去噪及其在信号检测中的应用[J]. 华中理工大学学报,1997,25(2):1-4
91. 胡昌华,张军波,夏军,等.基于 MATLAB 的系统分析与设计—小波分析[M].西安:电子科技大学出版社,1999
92. 冯林,成璐,吴振宇.基于提升算法的测井信号去噪处理[J] .石油物探,2004,43(3):238-241
93. 乔强,周激流,何坤等.基于小波变换的非平稳信号去噪[J].计算机应用研究,2005,(8):161-163
94. 潘泉,张磊,孟晋丽,等.小波滤波方法及应用[J].北京:清华大学出版社,2005
95. 郑继明.正交样条小波的指数衰减性[J].重庆邮电学院学报,2000,12(1):11-13
96. Dahmen W, Kunoth A, Urban K. Biorthogonal alpine wavelet on the interval-stability and moment conditions[J].Apllied and computa- tional harmonic analysis,1996(6): 132-296
97. 吴正毅.测试技术与测试信号处理[M].北京:清华大学出版社,1991
98. Petrou M, Stassopoulou A. Advanced techniques for fusion of information in remote sensing: an overview [A].Proc. SPIE[C], Florence, Italy, 1999,3871:264- 275
99. Bruno Aiazzi, Luciano Alparone, Fabrizio Argenti. Wavelet and pyramid techniques for multisensor data fusion: a performance comparion varying with scale ratios[A].Proc. SPIE[C], Florence, Italy, 1999,3871:251- 262
100. 晁锐,张科,李言俊.一种基于小波变换的图像融合算法[J] .电子学报,2004,32(5): 750-753
101. 林卉,杜培军,张莲蓬.基于小波变换的遥感影像融合与评价[J].煤炭学报,2005,30(3):332-336
102. 陈木生,狄红卫.多聚焦图像融合的最佳小波分解层研究[J] .光电工程,2004,31(3): 64-67
103. 雍世和,张超谟.测井数据处理与综合解释[M].山东东营:石油大学出版社,1996
104. 谭廷栋.测井学[M].北京:石油工业出版社,1998
105. 张庚骥.电法测井(上册)[M].北京:石油工业出版社,1984
106. 曾文冲.油气藏储集层测井评价技术[M].北京:石油工业出版社,1991
107. 王卫红,姜在兴,操应长,等.测井曲线识别层序边界的方法探讨[J].西南石油学院学报,2003,25(3):1-4
108. 郑小武,邓宏文,徐怀大等.测井高分辨率层序地层自动划分技术与应用尝试[J].石油与天然气地质.1999,20(4):357-360
109.李庆谋,刘少华.地球物理测井序列的小波波谱方法[J].地球物理学进展, 2002,17(12):78-83
110.李江涛,余继峰,李增学.基于测井数据小波变换的层序划分[J].煤田地质与勘探, 2004,32(2):48-50
111.Pati Y C, Krishnaprasad P S. Analysis an Synthesis of Feedforward Neural Networks Using Discrete Affine Wavelet Transformations[J].IEEE Trans on Neural Networks,1993,4(1):73-85
112.李全厚,李汉川,穆晓雁,等.新一代套管井储层评价技术—脉冲中子中子(PNN)测井[J] .国外测井技术,2004,19(4):11-15
113.洪有密.测井原理与综合解释[M]. 东营:石油大学出版社,2002
114. 任晓荣.核测井信号数字滤波器的设计与实现[J].传感器世界,2003,(6):11-16
115. 任晓荣,赵福宇.核测井信号数字滤波器的性能分析[J].核电子学与探测技术,2005,25(6):742-745
116. 王瑞平.岩石样品 NMR 驰豫信号的数学处理[J].测井技术,1997,26(6):397-400
117. Dines K, Lyttle J.Computerized geophysical tomography[J]. Proc IEEE,1979,67:1065-1073
118. 王为民,李培,叶朝辉.核磁共振驰豫信号的多指数反演[J] .中国科学(A 辑),2001,31(8):730-736
119. 翁爱华,李舟波,莫修文,等.核磁共振测井数据高分辨率反演方法研究[J] .测井技术,2002,26(6):455-459
120. 刘堂晏,肖立志,傅容珊,等.球管孔隙模型的核磁共振(NMR)驰豫特征及应用[J].地球物理学报,2004,47(4):663-671
121. 童茂松,李莉,王伟男,等.岩石激发极化驰豫时间谱与孔隙结构、渗透率的关系[J] .地球物理学报,2005,48(3):710-716
122. 肖立志.核磁共振成像测井与岩石核磁共振及其应用[M].北京:科学出版社,1998
123. 肖立志,石红兵.低场核磁共振岩心分析及其在测井解释中的应用[J] .测井技术,1998,22(1):42-49
124. 姚绪刚,王忠东.一种新的核磁共振驰豫谱反演算法[J] .测井技术,2003,27(5):373-376
125. 王丽丽,张一鸣,陈继忠.低场脉冲横向驰豫信号的反演分析[J].分析仪器,2005,(4):8-11
126. 王忠东,肖立志,刘堂宴.核磁共振驰豫信号多指数反演新方法及其应用[J].中国科学(G 辑),2003,33(4):323-331
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