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(完整版)AVO属性分析技术及其在油气检测中的应用_毕业设计论文

摘要随着油气勘探与开发程度的加深，常规的地震勘探方法难以检测到地层（岩性）油气藏的存在，这时可以使用AVO技术来辅助检测油气。

AVO技术最初用于识别“亮点”等振幅异常，随着近几年来计算机技术的发展和应用, 使AVO技术在油气勘探领域中拥有越来越广泛的应用。

本文着重从AVO技术的发展进程、AVO原理、技术思路、实例分析、优缺点等方面来论述AVO属性分析技术及其在油气检测中的应用。

关键词：AVO；AVO技术；泊松比；正演；反演；油气检测ABSTRACTWith the development of oil and gas exploration and the deepening of the degree of seismic exploration, conventional methods can not detect formation (lithology) oil and gas reservoir, then we use the A VO technique to detect oil and gas. A VO technology was used to identify the "bright spots" amplitude anomaly, as long as the development and application of computer technology in recent years, the A VO technology in the field of oil and gas exploration .In my paper, I’ll discuss the aspects of A VO attribute analysis technology and its application in oil and gas detection through the development process of A VO technology, A VO principle, technology, caseanalysis, advantages and disadvantages.keywords: A VO；AVO Technology；Poisson's ratio；Forward；inversion；Oil and gas detection；目录1 前言 (3)1.1 地震属性技术发展进程 (4)1.2 地震属性分类 (5)2 AVO技术 (5)2.1 AVO技术的现状与前景 (5)2.2 AVO技术的原理 (10)2.2.1 AVO技术的物理基础 (10)2.2.1.1 波动方程 (8)2.2.1.2 佐普里兹（Zoeppritz）方程组 (8)2.2.2 AVO技术的地质基础 (12)2.3 AVO技术思路 (11)2.4 AVO技术的应用 (12)2.4.1 识别真假亮点 (13)2.4.2 油气水边界检测 (15)2.4.3 解释岩性 (15)2.5 实例分析 (16)2.6 影响AVO分析的因素 (23)2.7 AVO技术的优势及局限性 (23)3 总结 (25)参考文献(References) (25)致谢 (26)1 前言随着社会的进步和工业的发展，油气资源被越来越多的用于工业生产与国民生活。

AVO基本概念

振幅系数的信息量
在一定的约束条件下，例如，最小平方约束，或对介质参数有足够的了解，是可以由振幅系数唯一地确定介质的属性的。若选择独立的参数是
2 1 2 , 1

1 , 1
2 , 2
令： 1 2 2 由于纵横波速度比与泊松比存在如下关系： 2
2 2
因此，独立的参数可以视为
3000
VS

(m/s)
(gm/cc) 2.02 2.12 2.2
1.
2.
左图说明了AVO分析的基本原
理，对于同一反射点而言，共中心点道集记录可用炮检距和深度等价表示入射角。对于理想的共中心点道集记录，含水砂岩AVO呈减少趋势，含气砂岩AVO呈增加趋势。不同的岩性参数组合，反射系数随入射角变化不同。
这表明：AVO信息有助于直接检测岩性和油气。传
AVO发展历史
Shuey(1985)对Zoeppritz的P波反射系数进行简
化，提出一种抛物线形式的表达，这使得AVO 属性分析和零炮检距剖面的提取得到广泛应用。 1985年，郑晓东在国内首先提出非零炮检距地震资料的正演和反演，并把AVO信息应用于“ 暗点”型气层的识别和检测。 Smith（1987）等提出用加权叠加方法估计流体因子和检测气层。为了充分挖掘AVO信息的潜力，不少作者研究用AVO属性（斜率和截距）交汇图识别岩性和油气的方法，并提出AVO烃类检测因子。
在碎屑岩中寻找天然气， AVO
理论是简单明了的，碎屑岩孔隙内含天然气，明显降低岩石的纵波（ P 波）速度，而横波（S波）速度相对保持不变。这就是说，当地层中含天然气时，会造成纵、横波速度比值的变化。这种变化必然导致在不同炮检距的反射振幅的分布，有着不同的表现。

AVO流体反演理论与实践

文章编号:100020747(2006)0520558204AVO流体反演理论与实践高建荣1,滕吉文2,李明3,张云绵3(1.吉林大学地球探测科学与技术学院;2.中国科学院地质与地球物理研究所;3.中国石油勘探开发研究院)基金项目:中国石油“全国石油预探区带综合评价与战略方向研究”(03011021)摘要:A VO流体反演技术能够定量地确定储集层含各种流体的可能性。

首先利用测井曲线建立Monte2Carlo随机正演模型,然后通过Biot2G assman流体替换理论获得模型中流体分别为油、气、水时的合成记录,利用Shuey公式得到相应的截距和梯度,将实际地震数据所得到的截距和梯度与模型所产生的截距和梯度进行对比,再利用Bayes理论即可定量求得所含油、气、水的可能性。

当储集层中含有油、气、水中的二相或三相时,可能性分布图重叠,预测的可能性值比只含单相流体时有所降低,但是预测的准确性仍然比较高。

将这项技术应用于川中天然气勘探,取得了较好的应用效果。

图8参8关键词:AVO;流体反演;Monte2Carlo;Biot2Gassman;Shuey公式;Bayes理论中图分类号:TE132.1 文献标识码:AAVO fluid inversion:theory and practiceGAO Jian2rong1,TEN G Ji2wen2,L I Ming3,ZHAN G Yun2mian3(1.College of Geo2Ex ploration Science&Technolog y,J ilin Universit y,J ilin130026,China;2.I nstitute of Geology and Geop hysics,CA S,B ei j ing100029,China;3.ResearchI nstitute of Pet roleum Ex ploration&Development,B ei j ing100083,China)Abstract:AVO fluid inversion can quantitatively determine the probability distribution of fluids in a reservoir.Procedures are as follows:establish a Monte2Carlo simulation model f rom logging curves,get the synthetic data using Biot2Gassman theory as the fluids are oil,gas,and water in the model,get intercept and gradient using Shuey’s function,compare the intercept and gradient values from the seismic data with the model values,and finally determine the probability of each fluid using Bayesian pared to the one phase fluid,when the fluid is composed of two or three phases,the predicted value is low owing to the overlapping of the probability distribution graph,but its precision is still high.The technology was successf ully applied in the middle Sichuan Province.K ey w ords:AVO;fluid inversion;Monte2Carlo;Biot2Gassman;Shuey’s f unction;Bayesian theory 长期以来,从地震AVO信息中获取流体相关信息是地球物理学界关注的焦点:文献[1]提出波长(λ)2剪切模量(μ)2密度(ρ)技术,文献[2]基于Murp hy等的工作提出了孔隙模量方法,文献[3]介绍了流体区分的概念,文献[4]利用AVO理论中的Biot2Gassman理论来区分流体属性[4]。

AVO基本概念

振幅系数的信息量
在一定的约束条件下，例如，最小平方约束，或对介质参数有足够的了解，是可以由振幅系数唯一地确定介质的属性的。若选择独立的参数是
2 1 2 , 1

1 , 1
2 , 2
令： 1 2 2 由于纵横波速度比与泊松比存在如下关系： 2
2 2
因此，独立的参数可以视为
AVO分析的理论基础程
1．反射和透射的统一公式
－－Zoeppritz方
假设有一平面纵波自介质I以入射角
入射到界面上，可能会产生四个波。它们分别是反射P波、透射P波、反射SV波和透射SV波。根据Snell 定律，反射P波的反射角为，设反射SV波的反射角为，透射P波和透射SV波的透射角分别为和，它们之间满足：
统的地震岩性分析方法是建立在水平叠加基础上的“ 亮点”技术，这种技术改善了地震烃类直接检测技术，但也存在明显的局限性。叠加破坏真实的振幅关系，损失了AVO隐含的横波信息。更为合理的地震岩性分析方法应该包括非零炮检距的地震信息，包括叠前振幅（或称AVO）和叠前地震属性（或称广义AVO）。
AVO发展历史
透射理论是地震勘探的理论基础，早在本世纪初已基本建立。我们知道，以法向入射到界面上的平面P波，在界面上不会转换成S波，通常情况下的反射系数公式就是针对这种情况导出的。
AVO分析的理论基础程
－－Zoeppritz方
除法向之外，以任何角度入射的P波，其中一部分能
量要转换成S波。在反射界面上，入射波的分配可以用几个方程组之中的任何一个来表示，常用的是利用入射与反射和透射的位移幅度表示。包含反射系数描述质点位移的方程，最经典的是 Zoeppritz方程（1919）。对于给定的反射界面， Zoeppritz方程的解取决于两种介质中的纵横波速度和密度差异，以及入射角。而纵横波速度比又直接反映在介质的泊松比上. 这些研究，使人们逐渐认识到，含气砂岩的泊松比异常，可以为从地震记录中识别含气砂岩提供了一种有效手段。

《AVO处理技术》课件

随着技术的不断进步和应用范围的不断扩大，AVO技术有望成为地球科学领域的重要支柱技术之一，为人类探索地球和开发资源提
供更加有力的支持。
THANKS
感谢观看
AVO技术通过对音频和视频的采集、编码、传输、解码、播放等各个环节进行优化处理，提高音频和视频的质量和用户体验。它主要通过对音频和视频的数据流进行分析和处理，消除噪声、增强音质、提高画质等方面进行优化，提供更加清晰、流畅、自然的音频和视频体验。
AVO技术的原理
AVO技术的原理： AVO技术主要基于数字信号处理技术和人工智能技术，通过分析和处理音频和视频数据流，实现各种优化功能。
复杂地质条件下的应用限制
在复杂的地质条件下，如非均匀、非层状的地下介质，AVO技术的准确性和可靠性可能会受到影响。
数据采集要求高
为了获得高质量的地震数据，AVO技术需要高精度的地震数据采集设备和技术，这可能会增加勘探成本。
数据处理难度大
由于AVO技术涉及到的数据处理和分析较为复杂，需要专业的技术人员和先进的计算机设备来完成。
详细描述
波形拟合方法是一种常用的AVO技术，它通过比较实际地震记录和理论地震记录，寻找最佳的地球物理参数，如介质的密度、速度和泊松比等。这种方法需要对地震波的传播规律和地球介质属性有一定的了解，通常需要借助地震学和地球物理学的理论模型。
波形反演方法
总结词
通过反演地震波在地下的传播过程，得到地下介质的物理参数。
详细描述
波形反演方法是一种直接的反演方法，它通过反演地震波在地下的传播过程，得到地下介质的物理参数，如介质的密度、速度和泊松比等。这种方法需要对地震波的传播规律和反演算法有一定的了解，通常需要借助数

AVO and AVA inversion challenges a conceptual overview

AVO and AVA inversion challenges: a conceptual overviewJeff P. GrossmanABSTRACTInversion of seismic data for earth parameters involves two main steps: (1) estimate the reflectivity as a function of incidence angle for each point in the subsurface, and (2) in accordance with some mathematical model, invert the reflectivity to estimate the corresponding earth parameters. As simple as this observation may seem at first sight, there are many challenges associated with both of these steps. In conjunction with the extensive, up to date bibliography compiled here, it is my hope that my humble perspective on these issues will benefit the active researcher of seismic inversion theory.OVERVIEWConventional AVO (amplitude variation with offset) analysis is based on the well-known Knott-Zoeppritz equations (Knott, 1899, and Zoeppritz, 1919). For a planar interface between two homogeneous isotropic elastic halfspaces in welded contact, these equations describe the various reflection and transmission coefficients for plane waves as a function of angle of incidence, the elastic constants and the densities of the two halfspaces (see e.g., Aki and Richards, 1980). Although these assumptions may appear rather restrictive, they nevertheless play a central role in the so-called forward problem of reflection seismology. This forward problem “consists of the determination of the data that would be recorded for a given subsurface configuration ... under the assumption that given laws of physics hold.” (Treitel and Lines, 2000).The corresponding inverse problem of reflection seismology, then, is nothing but the determination of the subsurface configuration from the observed data. What exactly is meant by subsurface configuration is open to debate. Practically speaking, I take it to mean the spatially varying elastic parameters -- this of course assumes that the data can be reasonably forward modelled by elasticity theory. Thus, for example, this assumes that any Q effects can be handled separately.Reflections arise from discontinuities in earth parameters, i.e., reflectors. They can also arise from continuously varying parameters, e.g., turning rays, but we will ignore these and focus our attention on the reflectors. Since reflection data are recorded in time, the data must be migrated in order to estimate the locations of reflectors in depth. Thus, at least in principle, migration plays a central role in our inverse problem. Migration can be viewed in itself as a type of inversion, being part of the more general inverse problem known as imaging.The sharpness of the image and the separation of nearby events are limited by our ability to remove the blurring effect of the seismic wavelet. Removal of this ambiguity is commonly known as deconvolution, yet another part of the inverse problem of seismic imaging. Ideally, each reflection event on each trace appears as a spike, located at the correct position (in time or depth), and having a distribution of amplitudes representing the reflection coefficients (Rpp, Rps, etc.) as a function of the unknown angle of incidence.Once we are given a reasonably clean, sharp image, and corresponding functions describing the amplitude variations with incidence angle at each point in the image, we are in a position to consider the problem of inverting the AVA (amplitude variation with angle) information for the earth parameters.We have neglected to mention some of the many stumbling blocks along the way: e.g., multiple reflections, multipathing, attenuation, anisotropy, acquisition geometry, data quality, ground roll, head waves, dispersion, “noise” such as power line hum, wind, traffic, or any other disturbance that fails to fit our simplified model, and the fact that seismic data are bandlimited and acquired with finite aperture. Thankfully, there are some good processing algorithms available to remove -- or at least reduce -- some of these problems.A major obstruction to solving the seismic inversion problem is nonuniqueness. Given any data set, infinitely many distinct earth models will fit the data to within any given measure of error. A priori information such as well logs or experience with regional geology must be incorporated to constrain the set of allowable models.The Zoeppritz equations are no longer sufficient if we are to include anisotropy in our model. In particular, Snell's law in its familiar form still applies to phase angles and phase velocities; but for rays, which are connected with group velocities and group angles, a generalized Snell's law is required (e.g., Slawinski et al., 2000). Daley and Hron (1977) (errata for that paper appear in Daley, 2002) develop reflection and transmission coefficients for the case of transversely isotropic media. Thomsen (1993) developed linear approximations to reflection coefficients for weak VTI media, incorporating his well-known anisotropy parameters, delta and epsilon, which he introduced earlier (Thomsen, 1986). However, some errors appeared in Thomsen's 1993 paper, which were later corrected by Rüger (1996).Considering the complexity involved, it is quite remarkable that exploration seismology has enjoyed the level success that it has. Jackson (1972) elaborated on the complications associated with the seismic exploration problem in his paper: “Interpretation of inaccurate, insufficient and inconsistent data”.AVO/AVA INVERSION FOR ELASTIC PARAMETERS There are two main steps to inverting seismic data for the elastic parameters: (1) estimate the reflectivity as a function of incidence angle for each point in the subsurface, and (2) in accordance with some mathematical model, invert the reflectivity to estimate the corresponding elastic earth parameters.The first step can be approached in one of at least two ways: (1) using a time-dependent mapping from offset to incidence angle (Bale, et al., 2001, Ostrander, 1984), or (2) via prestack depth migration (de Bruin, et al., 1990, Hanitzsch, 1995, Bleistein, et al., 2001, Zhang, et al., 2001, Kühl and Sacchi, 2003). There is little doubt that the second alternative has greater potential than the first, although the additional cost might only be justified in regions of significant complexity. As Christian Hanitzsch (1995) stated in his Ph.D. Thesis:Amplitude preserving prestack [depth] migration is the most sophisticated method to obtain [angle-dependent] reflectivity and replaces the techniques of binning, geometrical spreading correction, normal moveout (NMO) correction, dip moveout (DMO) correction, reflection angle estimation and zero offset (post-stack) migration in traditional amplitude versus offset (AVO) processing.The second step -- the inversion itself -- can be attempted in several ways. One way is to seek linearized approximations to the Zoeppritz equations, and then analytically solve these linear equations for the earth parameters. A second method is to determine a set of earth parameters that fit the data: that is, according to some error measure, minimize the difference between the angle-dependent reflectivity data and the forward-modelled synthetics obtained from trial earth parameters.Often, sands and shales cannot be distinguished based on velocities alone; and in such cases, density can be a better discriminator. However, it is well known that the linearized approximations to the Zoeppritz equation for Rpp (e.g. Aki and Richards, 1980, Shuey, 1985, Fatti, 1994, Ramos and Castagna, 2001, Ursenbach, 2002a, 2002b, 2003a, 2003b) are relatively insensitive to changes in density, especially for limited-aperture experiments (Lines, 1998, and Ursenbach, 2002). For seismic exploration, this often means that density contrast cannot be satisfactorily estimated from P-wave data alone.There is much demand for a robust method of density estimation, and since p-wave data alone has been shown to be generally insufficient, we naturally look toward multicomponent data to extract more information. Specifically, we are interested in PS converted waves, i.e., seismic energy originating from a compressional source, which partially converts upon reflection to shear wave energy. Although SS waves and SP converted waves are of theoretical interest, shear waves emanating from a point source (e.g., dynamite) tend to have much lower energies than compressional waves (although this is not universally agreed upon).Having opted to exploit converted wave data, the question arises as to whether the PP and PS data should be inverted jointly or sequentially (Treitel and Lines, 2000). Joint, or simultaneous, inversion (Stewart, 1990, Larsen, 1999, Larsen, et al., 2000, Ronen, et al., 2000, Henley, et al., 2002) presupposes that interpreted events on the PP and PS sections can be registered (to put in exact alignment, as in printing or colour photography). This registration process is only necessary if the analysis is taking place in the time domain, since the misalignment of events there is purely a consequence of the different velocities for the two propagation modes. In depth, assuming an accurate migration output, PP and PS events are of course registered automatically. This suggests a nice quality-control check, i.e., how well the PP and PS migration outputs register in depth. However, in practice this can be difficult to judge, since the two reflectivities can be quite different, and can even have opposite polarities at the same depth (G. Margrave, personal communication, 2003).There is also the problem of distinguishing events in the data among the various types of mode conversion. P-to-S converted reflections tend to arrive at near vertical trajectories, so they tend to be well represented by the radial component of the data. When the near-surface compressional velocities are very low compared to the deeperones, PP reflections also arrive vertically; thus, PP events appear mostly on the vertical component.In these typical situations, the data effectively separates itself into PP and PS events. However, an interesting exception to this rule is exploration over a very fast isotropic permafrost layer. Such a layer can seriously complicate the separation process since it deflects incoming rays away from the vertical.OPTIMAL ZOEPPRITZ APPROXIMATIONSCharles Ursenbach (2002a, 2003b) developed optimal pseudo-linear approximations to the Zoeppritz equations for Rpp and Rps. He calls these approximations pseudo-linear since they are expressed in a form similar to the approximations of Aki and Richards (1980) (in fact, their linear approximations are not, formally speaking, linear). Ursenbach’s expressions are optimal in the sense that they minimize error while preserving the familiar format of the Aki-Richards approximations. They have the advantage over other approximations of maintaining good accuracy even at post-critical angles of incidence. They also bridge the gap between the Aki-Richards approximations and the full Zoeppritz equations, in that they are accurate yet amenable to standard AVO methods (Ursenbach, 2003b).PARAMETER SELECTIONFor the mathematical inversion step, not all parameterizations of the Zoeppritz equations are equal. Debski and Tarantola, (1995) looked at certain probabilities associated with AVA inversion for different choices of parameter sets. They found that choosing the parameter set {density, P-velocity, and S-velocity} to invert AVA information is a mistake. Unfortunately, this happens to be a common choice. They offer several better alternatives: {density, P-impedance, Poisson's ratio}, {density, P-impedance, S-impedance}, or {density, P-impedance, Jussieu's ratio}, where Jussieu's ratio is defined as the ratio of bulk modulus over shear modulus. It is conceivable that improved density estimates could be obtained by adopting one of these recommended parameterizations.WHY PS AVO/AVA INVERSION?Large incidence angles for PS converted waves are typically achieved at shorter offsets than for PP reflections. This is because rays follow the path of least traveltime, and thus travel longer distances in the fastest direction or mode of propagation. This means that for a given aperture, more complete AVA information is available for PS data than for PP data; thus allowing for a more reliable parameter inversion. Moreover, at least in the absence of noise, inversion of converted wave data is more accurate than other sources (Ursenbach, 2003a). In the presence of noise, Joint PP and PS inversion is better than either alone given that events can be aligned (G. Margrave, personal communication, 2003; demonstrated by Larsen, 1999).Finally, converted wave data offers an extra, independent source of information. This leads to higher resolution elastic parameter estimates, and ultimately betters knowledge of subsurface rock properties.AVO IN TI MEDIAPP incidence angles increase more rapidly with offset in TI media, when the fast direction is horizontal, than they do in isotropic media. Therefore, PP AVO may be sufficient in many cases. However, as mentioned above, neither the Zoeppritz equations nor Snell's law apply any longer: the TI reflection coefficients are required (Daley and Hron, 1977, errata in Daley, 2002).CONCLUSIONSInversion of seismic data for earth parameters involves two main steps: (1) estimate the reflectivity as a function of incidence angle for each point in the subsurface, and (2) in accordance with some mathematical model, invert the reflectivity to estimate the corresponding earth parameters. Some of the many challenges associated with both of these steps have been reviewed. An extensive, up to date bibliography has been provided. The active researcher of seismic inversion theory should find use for this work either as a starting point, or as a refresher on the subject.ACKNOWLEDGEMENTSThis work was made possible by the generous support of Veritas DGC Inc. I had the pleasure of holding a position at Veritas in the summer of 2003, and this paper constitutes a large part of the research I conducted then. I would like to thank the following people, in alphabetical order, for very helpful discussions pertaining to AVO analysis, and imaging and inversion: Paul Anderson, Richard Bale, Norm Bleistein, Jon Downton, Jon Gittins, Bill Goodway, David Gray, Jay Gunderson, Rob Kendall, Michael Lamoureux, Gary Margrave, Rachel Newrick, Arunas Saulkauskas, Michael Slawinski, Daniel Trad, Charles Ursenbach, and James Wookey.REFERENCESAki, K. and Richards, P. G., 1980, Quantitative Seismology: Theory and Methods: W. H. Freeman & Co. Bale, R., Leaney, S., and Dumitru, G., 2001, Offset-to-angle transformations for PP and PS AVO analysis: 71st Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts.Barnes, A. E., 1992, Another look at NMO stretch: Geophys., 57, 749-751.Bucholtz, H., 1972, A note on signal distortion due to dynamic (NMO) corrections: Geophys. Prosp., 20, 395-402.Carcuz, J. R., 2001, A combined AVO analysis of P-P and P-S reflection data: 71st Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts.Castagna, J. P., Batzle, M. L., and Eastwood, R. L., 1985, Relationship between compressional-wave and shear-wave velocities in clastic silicate rocks: Geophys., 50, 571-581.Castagna, J. P., and Smith, S. W., 1994, Comparison of AVO indicators: A modeling study: Geophys., 59, 1849-1855.Chen, Y. M., 1985, Generalized pulse spectrum technique: Geophys., 50, 1644-1675.Daley, P. F., and Hron, F., 1977, Reflection and transmission coefficients for transversely isotropic solids: Bull. Seis. Soc. Amer., 67, 661-675.Daley, P. F., 2002, Errata: Reflection and transmission coefficients in T.I. media: CREWES Research Report, 14.de Bruin, C. G. M., Wapenaar, C. P. A., and Berkhout, A. J., 1990, Angle-dependent reflectivity by means of prestack migration: Geophys., 55, 1223-1234.Debski, W., and Tarantola, A., 1995, Information on elastic parameters obtained from the amplitudes of reflected waves: Geophys., 60, 1426-1436.Downton, J. E., and Lines, L. R., 2001, Constrained three parameter AVO inversion and uncertainty analysis: 71st Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts.Downton, J. E., and Lines, L. R., 2002, AVO NMO, 72nd Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts.Dunkin, J. W., and Levin, F. K., 1973, Effect of normal moveout on a seismic pulse: Geophys., 38, 635-642.Fatti, J. L., Smith, G. C., Vail, P. J., Stauss, P. J., Levitt, P. R., 1994, Detection of gas in sandstone reservoirs using AVO analysis: A 3-D seismic case history using the Geostack technique: Geophys., 59, 1362-1376.Gardner, G. H. F., Gardner, L. W., and Gregory, A. R., 1974, Formation velocity and density -- The diagnostic basics for sedimentary traps: Geophys., 39, 770-780.Gazdag, J., 1978, Wave equation migration with the phase-shift method: Geophys., 43, 1342-1351. Gidlow, P. M., Smith, G. C., and Vail, P.\ J., 1992, Hydrocarbon detection using fluid factor traces: a case history, SEG/EAEG Summer Research Workshop, Technical Program and Abstract, 78-89. Goodway, W., Chen, T., and Downton, J., 1997, Improved AVO fluid detection and lithology discrimination using Lamé physical parameters; “Lambda-Rho”, “Mu-Rho”, & “Lambda/Mu fluid stack”, from P and S inversions: 67th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts.Gray, D., Goodway, W., and Chen, T., 1999, Bridging the gap: using AVO to detect changes in fundamental elastic constants: 69th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts. Grossman, J. P., Margrave, G. F., Lamoureux, M. P., and Aggarwala, R., 2002a, Constant-Q wavelet estimation via a Gabor spectral model: CSEG Convention Expanded Abstracts.Grossman, J. P., Margrave, G. F., Lamoureux, M. P., and Aggarwala, R., 2002b, A robust algorithm for constant-Q wavelet estimation using Gabor analysis: 72nd Ann. Internat. Mtg., Soc. Expl.Geophys., Expanded Abstracts.Grossman, J. P., Margrave, G. F., and Lamoureux, M. P., 2003, Fast wavefield extrapolation by phase shift-in the nonuniform Gabor domain: CSEG Expanded Abstracts, and PIMS Summer Workshop in Geophysical Inversion.Hicks, G. J., 2001, Removing NMO stretch using the Radon and Fourier-Radon transforms: 63rd Ann.Mtg., Eur. Assn. of Expl. Geophys., A-18.Jackson, D. D., 1972, Interpretation of inaccurate, insufficient and inconsistent data: Geophys. J. Roy. Astr.Soc., 28, 97-109.Hanitzsch, C., 1995, Amplitude preserving prestack Kirchoff depth migration/inversion in laterally inhomogeneous media: Ph.D. Dissertation, Karlsruhe.Henley, D. C., Margrave, G. F., and Zhang, H., 2002, Preparing input data for joint PP/PS inversion: CREWES Research Report, 14.Knott, C. G., 1899, Reflection and refraction of elastic waves with seismological applications: Philosophical Magazine, Series 5, 48, 64-97.Kühl, H., and Sacchi, M. D., 2003, Least-squares wave-equation migration for AVP/AVA inversion: Geophys., 68, 262-273.Larsen, J. A., 1999, AVO inversion by simultaneous P-P and P-S inversion: M.Sc. Thesis, University of Calgary, Dept. of Geology and Geophysics.Larsen, J. A., Margrave, G. F., Stewart, R. R., and Li, X., 2000, Simultaneous PP and PS inversion by weighted stacking, SEG/EAGE Summer Research Workshop.Lines, L. R., 1998, Density contrast is difficult to determine from AVO: CREWES Research Report, Vol.10.Lines, L. R., and Treitel, S., 1984, Tutorial: A review of least-squares inversion and its application to geophysical problems: Geophys. Prosp., 32, 159-186.Ostrander, W. J., 1984, Plane-wave reflection coefficients for gas sands at nonnormal angles-of-incidence: Geophys. Prosp., 35, 993-1014.Özdemir, H., Ronen, S., Olofsson, B., Goodway, W., and Young, P., 2001, Simultaneous multicomponent AVO inversion: 71st Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts.Potter, C. C., Dey, A. K., and Stewart, R. R., 1998, Density prediction using P- and S-wave sonic velocities: Geotriad, 1998 CSPG, CSEG, CWLS Joint Convention.Ramos, A. C. B., and Castagna, J. P., 2001, Useful approximations for converted-wave AVO: Geophys., 66, 1721-1734.Ronen, S., Goodway, B., Young, P., Özdemir, H., and Engelmark, F., 2000, Simultaneous Multicomponent AVO Inversion, SEG/EAGE Summer Research Workshop.Rüger, A., 1996, Reflection coefficients and azimuthal AVO analysis in anisotropic media, Ph.D. Thesis, Colorado School of Mines.Rupert, G. B. and Chun, J. H., 1975, The block move sum normal moveout correction: Geophys., \QTR{bf}{40}, 17-24.Shuey, R. T., 1985, A simplification of the Zoeppritz equations: Geophys., 50, 609-614.Sivia, D. S., 1996, Data Analysis, A Bayesian tutorial: Oxford University Press.Slawinski, M. A., Slawinski, R. A., Brown, R. J., and Parkin, J. M., 2000, A generalized form of Snell's law in anisotropic media: Geophys., \QTR{bf}{65}, 632-637.Smith, G. G., and Gidlow, P. M., 1987, Weighted stacking for rock property estimation and detection of gas: Geophys. Prosp., 35, 993-1014.Stewart, R. R., 1990, Joint P and P-SV Inversion: CREWES Research Report, Vol. 2, 112-115.Stolt, R. H., 1978, Migration by Fourier transform: Geophys., 43, 23-48.Swan, H. W., 1997, Removal of offset-dependent tuning in AVO analysis, 67th Ann. Intl. Mtg.: Soc. of Expl. Geophys., 175-178.Thomsen, L., 1986, Weak elastic anisotropy, Geophys., 51, 1954-1966.Thomsen, L., 1993, Weak anisotropic reflections, in Offset dependent reflectivity, Castagna and Backus, eds., SEG, Tulsa.Tarantola, A., 1986, A strategy for nonlinear elastic inversion of seismic reflection data: Geophys., 51, 1893-1903.Treitel, S., and Lines, L. R., 2000, Past, Present and Future of Geophysical Inversion -- a Y2K Analysis: GeoCanada Conference Expanded Abstracts.Trickett, S. R., 2003, Stretch-free stacking: National Convention Can. Soc. of Expl. Geophys., Expanded Abstracts.Tsvankin, I., and Thomsen, L., 1994, Nonhyperbolic reflection moveout in anisotropic media: Geophys., 59, 1290-1304.Tygel, M., Santos, L. T., and Schleicher, J., 1999, Kirchoff imaging as a tool for AVO/AVA analysis: The Leading Edge, 18, 940-945.Ursenbach, C., 2002a, Optimal Zoeppritz approximations: 72nd Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts.Ursenbach, C., 2002b, Research note: Improved approximations for anisotropic reflectivities: CREWES Research Report, Vol. 14.Ursenbach, C., 2003a, Can multicomponent or joint AVO inversion improve impedance estimates?: 73rd Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts.Ursenbach, C., 2003b, Extension and evaluation of pseudo-linear Zoeppritz approximations: CSEG Expanded Abstracts.Verm, R., Hilterman, F., 1995, Lithology color-coded seismic sections: The calibration of AVO crossplotting to rock properties: The Leading Edge, 14, 847-853.Walden, A. T., 1991, Making AVO sections more robust: Geophys. Prosp., 39, 915-942.Yilmaz, Ö., 2000, Seismic Data Processing: Society of Exploration Geophysicists.Zhang, G. Q., 1993, System of coupled equations for up-going and down-going waves: Acta Math. Appl.Sinica, 16, no. 2, 251-263.Zhang, H., and Brown, R. J., 2001, A review of AVO analysis: CREWES Research Report, Vol. 13. Zhang, Y., Gray, S., and Young, J., 2001, True-amplitude common-offset, common-azimuth v(z) migration: Journal of Seismic Exploration (to appear).Zhang, Y., Sun, J., Gray, S., Notfors, C., and Bleistein, N., 2001, Towards accurate amplitudes for one-way wavefield extrapolation of 3-D common shot records: 71st Ann. Internat. Mtg., Soc. Expl.Geophys., Expanded Abstracts.Zhang, Y., Zhang, G. Q., and Bleistein, N., 2003, True amplitude wave equation migration arising from true amplitude one-way wave equations: CWP Project Review Report, Colorado School of Mines. Zoeppritz, K., 1919, Erdbebenwellen VIIIB, On the reflection and propagation of seismic waves: Göttinger Nachrichten, I, 66-84.。

avo反演matlab程序

avo反演matlab程序AVO反演（Amplitude Versus Offset）是一种地球物理方法，用于从地震数据中推断岩石的弹性参数，以便更好地了解地下结构。

MATLAB是一个广泛使用的科学计算和数据可视化软件，有着丰富的工具箱和函数库，可以用于编写AVO反演的程序。

本文将介绍如何使用MATLAB编写AVO反演程序。

首先，我们需要准备一些地震数据。

地震数据通常以二维或三维地震剖面的形式存在，其中包含了大量振幅和偏移信息。

为了方便演示，我们可以使用MATLAB的示例数据来进行AVO反演。

```MATLABdata = load('seismic_data.mat'); % 导入示例地震数据trace = data.seismic_data; % 提取地震剖面中的一条道```接下来，我们可以对地震数据进行预处理，包括去噪和平滑处理。

可以使用MATLAB的滤波函数或者小波变换函数来实现。

```MATLABnoisy_trace = wdenoise(trace, 'Wavelet', 'haar'); % 使用小波变换去噪smooth_trace = smoothdata(noisy_trace, 'gaussian', 10); % 使用高斯平滑滤波平滑数据```在AVO反演中，我们需要定义合适的模型来描述地下的波速和泊松比分布。

常用的模型包括背景模型和岩性模型。

背景模型用于描述整个区域的基本特征，而岩性模型用于描述特定地层的参数变化。

我们可以使用MATLAB的矩阵和数组来定义模型。

```MATLABbackground_velocity = 2000; % 背景波速background_density = 2200; % 背景密度rock_velocity = [2300, 2400, 2500]; % 岩石波速rock_density = [2300, 2400, 2500]; % 岩石密度```在进行AVO反演之前，我们需要对地震数据进行预处理，以提取出合适的特征用于反演。

利用地震AVO反演预测煤与瓦斯突出区

非常规天然气收稿日期:2010-12-03;修回日期:2011-04-271基金项目:国家/9730项目(编号:2009CB219603;2009C B72460;2010CB226800)联合资助.作者简介:胡朝元(1963-),男,河北石家庄人,高级工程师,主要从事三维地震资料解释技术研究.E -mail:hcy0315@s .通讯作者:杜文凤.E -mail:duw f66@.利用地震A VO 反演预测煤与瓦斯突出区胡朝元1,2,彭苏萍1,杜文凤1,勾精为1,2(1.中国矿业大学煤炭资源与安全开采国家重点实验室,北京100083;2.中国煤炭地质总局地球物理勘探研究院,河北涿州072750)摘要:基于地震AVO 反演原理和方法,针对已知的煤与瓦斯突出点进行了AVO 反演。

单点分析发现,煤与瓦斯突出点处的偏移距)振幅拟合关系的截距和梯度绝对值比非突出点大,表明煤与瓦斯突出点能引起地震AVO 响应异常。

依据突出点处的地震AVO 响应特征,通过交会分析和综合指标分析,可实现对煤与瓦斯突出区的预测。

但是,作为一项新的预测技术,在实际应用中要结合区域地质综合分析,以避免AVO 反演结果的多解性。

关键词:煤与瓦斯突出;AV O 反演;地震勘探中图分类号:T E132.2 文献标识码:A 文章编号:1672-1926(2011)04-0728-05引用格式:胡朝元,彭苏萍,杜文凤,等.利用地震AVO 反演预测煤与瓦斯突出区[J].天然气地球科学,2011,22(4):728-732.0 引言煤与瓦斯突出是影响煤矿安全生产的重大问题之一,其预测方法主要有地面区域性预测、采面超前探测和巷道瓦斯检测。

实践研究证明,瓦斯突出区煤岩通常是破碎的,甚至呈粉末状,且瓦斯含量异常高。

因此,突出区煤岩物性(如波阻抗、密度、剪切模量、弹性模量等)存在异常[1-2]。

这种异常会引起地震勘探中AVO (Am plitude Versus Offset,即振幅随偏移距的变化)异常。

基于Gray方程的三参量AVO反演及煤层气富集区预测

基于Gray方程的三参量AVO反演及煤层气富集区预测杨臣明【摘要】Elastic parameters of coal reservoir can be got directly through AVO inversion to guide the evaluation of reservoir.Based on the introduction of the Gray approximate equation,the equation is used to study the three-parameter AVO inversion,the density,the bulk modulus and the shear modulus of CBM reservoir can be calculateddirectly.According to the characteristics of three elastic parameters in CBM enrichment zone,CBM enrichment zone was delineated after comparing the three elastic parameters.Finally,through model and case analysis,the feasibility of the method was verified.%通过AVO(Amplitude Versus Offset)反演可直接求取煤层气储层的弹性参数,指导储层的评价工作.在介绍Gray近似方程的基础上,应用该方程进行了三参量AVO反演方法的研究,直接计算出了煤层气储层的密度、体积模量和剪切模量;依据煤层气富集区3个弹性参数的特征,对比求取3个弹性参数剖面后,圈定出煤层气富集区;最后,通过模型和实例分析,验证该方法的可行性.【期刊名称】《煤田地质与勘探》【年(卷),期】2017(045)004【总页数】4页(P141-143,148)【关键词】AVO技术;Gray方程;三参量;煤层气【作者】杨臣明【作者单位】中国煤炭地质总局地球物理勘探研究院,河北涿州072750【正文语种】中文【中图分类】P631.4AVO(Amplitude Versus Offset)反演技术是利用反射系数随入射角变化的原理，在叠前道集上分析反射波振幅随偏移距变化的规律，是估算岩石各项弹性参数的重要技术。

AVO技术详解

第6章 AVO技术详解AVO技术是利用反射系数随入射角变化的原理，在叠前道集上分析振幅随偏移距变化的规律，估求岩石的弹性参数、研究岩性、检测油气的重要技术。

AVO是振幅随偏移距变化(Amplitude Variation with Offset)的英文缩写或振幅与随偏移距关系(Amplitude Versus Offset) 的英文缩写，AVA 是振幅随入射角变化(Amplitude Variation with Incident Angle)的英文缩写。

在地震勘探中，共中心点道集记录的偏移距可以等价地用入射角表示，故AVO与AVA等价。

该技术自20世纪80年代提出以来，在油气勘探中不断发展，并得到迅速推广与广泛应用。

尤其是在天然气勘探中指导寻找天然气藏发挥了重要作用，对提高天然气勘探成功率受到了很好的效果。

从近几年的技术发展情况看，P波方位AVO已作为一种预测油气藏各向异性的有效方法而受到青睐。

6.1 AVO技术的理论基础根据地震波动力学中反射与透射的相关理论，反射系数(或振幅)随入射角的变化与分界面两侧介质的地质参数有关。

这一事实包含两层意思：一是不同的岩性参数组合，反射系数(或振幅)随入射角变化的特性不同，称为AVO正演方法；二是反射系数(或振幅)随入射角变化本身隐含了岩性参数的信息，利用AVO 关系可以反演岩石的密度、纵波速度与横波速度，称为AVO 反演方法。

6.1.1 Zoeppritz 方程AVO 技术的理论基础就是Zoeppritz 方程及其简化的思路。

设有两层水平各向同性介质，当地震纵波非垂直入射(即非零偏移距)时，在弹性分界面上会产生反射纵波、反射横波、透射纵波与透射横波，见图6—1。

各种波型之间的运动学关系服从斯奈尔定理22221111sin sin sin sin S P S P V V V V ϕθϕθ=== (6-1)图6—1 入射波、反射波与透射波的关系式中 1θ、1ϕ——纵波、横波的反射角；2θ、2ϕ——纵波、横波的透射角；1P V 、2P V ——反射界面上下介质的纵波速度；1S V 、2S V ——反射界面上下介质的横波速度。

04_AVO理论

Vs 1 r 2 R( ) (1 4 2 sin ) 2 r Vp
2
V p 1 2 2 cos V p Vs Vs 2 4 2 sin Vs Vp
2
10
Notice that:
1 2 / 2
And also
Average between angle of reflection and angle of transmission 入射角和反射角的平均值
.
当小角度入射时，反射系数主要取决于P波的速度；当宽角入射时，反射系数主要取决于泊松比的差。
9
4
Rp G
17
利用A & R近似式可以得到如下反演结果
Impedance Attributes(阻抗属性体) 1，P-wave Impedence Reflectivity ------- P波阻抗反射率 2，S-wave Impedence Reflectivity ------- S波阻抗反射率 Velocity Attributes(速度属性体) 3，P-wave velocity Reflectivity ----------- P波速度反射率 4，S-wave velocity Reflectivity ----------- S波速度反射率 5，Pseudo-Poission Reflectivity -----------伪泊松比反射率 6，Fluid Factor -------------------------------流体因子 Elastic Modulus Attributes(弹性模量属性体) 7，Lame Constant Relative Changes ----- 拉梅常数相对变化 8，Shear Modulus Relative Changes ----- 剪切模量相对变化 9，Shear Modulus Reflectivity -------------剪切模量反射率 10，QC Factor -------------------------------- QC因子 18

基于叠前深度偏移的AVO反演及解释

基于叠前深度偏移的ＡＶＯ反演及解释
刘洪林，朱秋影
廊坊０６５００７）（１．大庆石油学院地球科学学院，大庆１６３３１８；２．中石油勘探开发研究院廊坊分院，
摘
要
本文针对复杂构造地区ＡＶＯ技术应用的难点，在详细阐明了偏移对改善ＡＶＯ分析效果的重要意义前提
法的可行性．
关键词叠前深度偏移，ＡＶ０反演，ＡＶ０属性，ＡＶ０异常中图分类号Ｐ６３１文献标识码Ａ文章编号ｌ００４ — ２９０３（２００７）０３ — ０９０５ — ０８
（１．ＣｏｌｌｅｇｅｏｆＥａｒｔｈＳｃｉｅｎｃｅ，ＤａｑｉｎｇＰｅｔｒｏｌｅｕｍＩｎｓｔｉｔｕｔｅ，Ｄａｑｉｎｇ１６３３１８，Ｃｈｉｎａ；２．ＬａｎｇｎｇＢｒａｎｃｈｏｆＰｅｔｒｏＣｈｉｎａＲｅｓｅａｒｃｈＩｎｓｔｉｔｕｔｅｏｆＰｅｔｒｏｌｅｕｍＥｘｐｌｏｒａｔｉｏｎａｎｄＤｅｖｅｌｏｐｍｅｎｔ，Ｌａｎｇｆａｎｇ０６５００７，Ｃｈｉｎａ）
下，为实现在复杂构造地区有效地进行ＡＶＯ研究，将叠前深度偏移引入到ＡＶＯ处理中，通过偏移来改善资料的质量．在此基础上进行了揭示岩性及流体变化特征的多种参数的ＡＶＯ反演，并探讨了利用多种ＡＶＯ属性的综合分析来确定和解释ＡＶＯ异常的方法．利用本文提出的复杂构造ＡＶＯ研究思路对实际资料进行了分析，结果说明了该方

AVO反演总结

P-Wave Impedance Reflectivity
P 波阻抗变化率： ΔIp = Δ(Vp ⋅ ρ )
Ip Vp ⋅ ρ
S-Wave Impedance Reflectivity
S 波阻抗变化率： ΔIs = Δ(Vs ⋅ ρ )
Is Vs ⋅ ρ
P-Wave Velocity Reflectivity S-Wave Velocity Reflectivity Pseudo-Poisson Reflectivity
Normal Reflectivity
Incidence NI 或 R0，入射角为零时的反射系数，所以又称零炮检距剖面
Gradient （ G）
梯度剖面
Sign(NI)*GR
NI 与梯度（ GR）的乘积剖面
Poisson Reflectivity
泊松比变化率： R0+G
P 波，速度为
Vp =
k + 34 μ ρ
S 波，速度为
Vs =
μ ρ
（花岗岩：VP=5.5 km/s,VS=3.0 km/s；水：VP=1.5 km/s, VS=0 km/s）墨西哥湾统计出来的泥岩基线关系函数： Vs＝0.86 Vp－1172 Gardner 密度公式
二、反射方程
Aki Richards 近似公式 :
AVO 知识总结
一、岩石物理理论体积模量 K－表示不可压缩性，花岗岩：k 约为 27×1010 达因/厘米 2 剪切模量 μ－表示其刚性，水：μ 为 0；花岗岩：μ 约为 1.6×1010 达因/厘米 2 拉梅常数 λ＝K－μ*2／3 密度为 ρ 的弹性固体内，可以传播两种弹II类

avo反演matlab程序

avo反演matlab程序以下是关于avo反演（Amplitude versus Offset inversion，简称avo 反演）的Matlab 程序的详细说明。

我们将一步一步回答您的问题，并附上必要的代码和解释。

1. 什么是avo反演？Amplitude versus Offset inversion（avo反演）是地球物理学中的一种分析方法，主要用于从地震数据中获取地下地质信息。

通过对反射波振幅和偏移距的变化关系进行分析，avo反演可以提供地下岩石的弹性参数等重要信息。

它在石油勘探和地下水资源调查中具有广泛的应用。

2. Matlab程序实现avo反演的基本步骤如何？a. 数据预处理：在实施avo反演之前，首先需要对地震数据进行预处理。

这涉及到对数据进行去噪、时距校正等操作。

Matlab提供了多种函数和工具箱来实现这些操作。

例如，您可以使用Matlab的'detrend' 函数来去除趋势项和去掉噪声。

您还可以使用'interp1' 函数进行时距校正。

b. avo反演算法：avo反演通常通过拟合Kiessling方程（aVO模型）的方法进行。

该方程描述了反射系数与角度以及岩石物性之间的关系。

具体的avo反演算法会根据数据的特点和需要进行定制开发。

以下是一个基本的avo反演算法的示例：MATLABfunction [vp, vs, rho] = avo_inversion(angle, reflectivity) % angle: 角度数据，单位为度% reflectivity: 反射系数数据% 设置默认参数vp0 = 2500; % 初始纵波速度模型vs0 = 1500; % 初始横波速度模型rho0 = 2000; % 初始密度模型iter = 10; % 迭代次数% 初始化速度和密度模型vp = vp0 * ones(size(angle));vs = vs0 * ones(size(angle));rho = rho0 * ones(size(angle));% 开始迭代for i = 1:iter% 计算反射系数的模拟值reflectivity_pred = avo_model(vp, vs, rho, angle);% 计算残差residual = reflectivity - reflectivity_pred;% 更新速度和密度模型[vp_update, vs_update, rho_update] = avo_update(vp, vs, rho, angle, residual);vp = vp + vp_update;vs = vs + vs_update;rho = rho + rho_update;endendfunction reflectivity_pred = avo_model(vp, vs, rho, angle) % avo模型计算反射系数的模拟值% vp, vs, rho: 各层速度和密度模型% angle: 角度数据，单位为度reflectivity_pred = zeros(size(angle));for i = 1:length(vp)reflectivity_pred(i) = (vp(i) - 2 * vs(i)) / (vp(i) + 2 * vs(i)) * sind(angle(i)) ^ 2;endendfunction [vp_update, vs_update, rho_update] = avo_update(vp, vs, rho, angle, residual)% avo反演更新速度和密度模型% vp, vs, rho: 速度和密度模型% angle: 角度数据，单位为度% residual: 反射系数残差vp_update = zeros(size(vp));vs_update = zeros(size(vs));rho_update = zeros(size(rho));for i = 1:length(vp)vp_update(i) = 0.1 * residual(i) * (2 * vs(i)) / (vp(i) + 2 * vs(i)) * sind(angle(i)) ^ 2;vs_update(i) = -0.1 * residual(i) * (vp(i) - 2 * vs(i)) / (vp(i) + 2 * vs(i)) * sind(angle(i)) ^ 2;rho_update(i) = 0.1 * residual(i) * (vp(i) - 2 * vs(i)) * (vp(i) +2 * vs(i)) * cosd(angle(i)) / (vp(i) + 2 * vs(i)) ^ 2;endend3. 如何使用Matlab进行avo反演？在上述例子中，我们定义了三个输入参数：angle（角度数据，单位为度），reflectivity（反射系数数据），和一个可选的迭代次数参数iter。

inversion名词解释

inversion名词解释
嘿，你知道啥是 inversion 不？Inversion 啊，就像是一场奇妙的变身！比如说吧，本来好好的顺序，突然就给颠倒过来啦，就像你本来是先
穿鞋子再出门，结果变成先出门再穿鞋子，这多有意思呀！这就是一
种 inversion 呀。

想象一下，音乐里也有 inversion 呢！本来高音在上面，低音在下面，突然来个颠倒，高音跑到下面去了，低音反倒在上面了，这就给音乐
带来了不一样的感觉，就好像原本走的平坦大路，突然变成了曲折小径，充满了新奇和挑战。

还有呢，在语言里也有 inversion 的身影哦！有时候为了强调某个东西，句子的结构就会发生变化，本来应该先说的放到后面去了，这就
像是给句子来了个大变身。

比如说“多美的花呀”，要是正常说可能就
是“这花很美”，但这样一颠倒，那种惊叹和赞美的感觉就更强烈啦，
对吧？
再看看我们的生活，有时候不也会有这种 inversion 吗？就像本来你
每天都是按时起床吃饭上班，突然有一天决定先去公园溜达一圈再做
其他的，这就是生活中的一种 inversion 呀，给平淡的日子带来了不一
样的色彩。

Inversion 可不只是简单的颠倒，它是一种创造力的体现呀！它能让
我们从熟悉的事物中看到不一样的一面，就像打开了一扇新的窗户，
让我们看到了以前没看到过的风景。

它可以让音乐更动人，让语言更有魅力，让生活更有趣味。

所以呀，inversion 真的是个超级神奇的东西呢，难道不是吗？
我的观点就是：inversion 是一种能够带来新奇、变化和创造力的现象，无论是在艺术、语言还是生活中，都有着独特的魅力和价值。

AVO三参数反演方法研究的开题报告

AVO三参数反演方法研究的开题报告一、选题背景AVO是地震勘探中普遍应用的一种方法，通过地震波在不同介质中的传播特性，解析出地下岩石的物理参数，如P波波速、S波波速、密度等。

AVO三参数反演方法是一种常用的地震数据处理方法，其基本思想是利用Pre-Stack数据拟合一组经验性的反射系数数据，然后通过采用岩石物理模型，以反射系数为约束条件，反演出地震数据中的P波波速、S波波速和密度等三个参数。

该方法在油气勘探中具有广泛的应用。

近年来，通过将AVO与机器学习方法结合，也取得了一些类似于深度学习的成果。

二、研究目的AVO三参数反演方法在油气勘探业中应用广泛，但是目前该方法在处理复杂地质条件下的数据时存在一些困难，比如储层非均质、有掩埋、开发程度较高等情况。

因此，本研究旨在通过对AVO三参数反演方法的研究和改进，提高该方法在复杂地质环境下的适用性和可靠性。

三、研究内容1. AVO三参数反演方法的理论分析AVO三参数反演方法基于真实物理模型，其反演结果具有理论基础。

因此，在研究该方法的应用范围和深层次分析的基础上，将对其理论基础进行系统梳理和分析。

2. AVO三参数反演方法在储层评价中的应用通过对储层进行分类，对比分析AVO三参数反演方法在不同类别的储层中的应用效果，以科学合理的理论研究和实验数据论证为基础，找到更为实用的应用方法。

3. AVO三参数反演方法与机器学习的融合通过将机器学习方法结合到AVO三参数反演方法中，探索解决本研究中所遇到的困难的可能性和现实应用前景。

四、研究意义1. 增加在复杂地质环境下的数据处理的稳定性和可靠性；2. 帮助油气勘探人员深入理解AVO三参数反演方法的机理及应用范围，优化实践中的应用效果；3. 为寻求优化AVO三参数反演方法的发展思路，为区域油气勘探开发提供理论支持；4. 实验结果旨在促进油气勘探领域的科学研究、数据分析和技术演进。

av综合征PPT课件

av综合征
临床表现
(5)X-现象(X-phenomenon)：即在原眼位时正位或轻度外斜，向正上方或正下方看时外斜度均增大，呈“X”形。 (6)XA-现象(X and A phenomenon)：即原眼位时轻度外斜，向正上方注视时的外斜度数增大较向正下方注视时外斜度数增大得少。 (7)XV-现象(X and V phenomenon)：即原眼位时轻度外斜，向正上方注视时的外斜度数增大较向正下方注视时外斜度数增大得多。 (8)◇-现象(◇-phenomenon)：即原眼位时，内斜度数较小或无内斜，向正上和正下方注视时内斜度数增加。
av综合征
概述
A-V综合征不是一种特定临床疾病，而是一种现象，称之为现象比称为综合征更为合理，但多数学者仍以A-V综合征来命名之。 Urist曾以“继发性垂直向偏位的水平斜视”首先详细报道了本病，故称为Urist综合征，于1958年命名为A-V综合征。国内陈斯同、刘家琦1984年首次报道本病。
av综合征
流行病学
A-V综合征在水平斜视中并不少见，据Urist1951年报道615例水平斜视中50%属于A-V综合征。Costenbader报道占12.5%～25%，其报道的412例A-V综合征中V-内斜占41%，V-外斜23%，A-内斜 25%，A-外斜1%。我国1963年统计12岁以下儿童各种斜视457例， A-V综合征30例占6.56%，其中V-内斜占56.66%，V-外斜23.33%， A-内斜16.66%，A-外斜3.45%。本征的斜视发生甚早，且常为先天性的，斜视性弱视少见，占12%～14%，多无明显屈光不正， 11%患者头位异常。
av综合征
发病机制
von Noorden认为斜肌功能异常是A-V综合征较常见原因，有斜肌功能异常的A-V综合征常引起旋转性斜视。由斜肌功能异常的 A-V综合征产生旋转性斜视，以水平肌止端倾斜矫正后，其旋转斜视不能矫正。而这种旋转斜视用眼底照相检查证实。 3.上、下直肌的原因 Brown认为上、下直肌的功能强弱是形成 A-V征的一个原因，由于这两条肌肉均有使眼球内转的次要作用，所以当上直肌力量过强时，可致A现象；而上直肌力量较弱时则致V现象；下直肌力量较强时，可致V现象；下直肌力量较弱致A现象。

AVO常见问题解答

AVO常见问题解答AVO 常见问题解答注：本人英文水平有限，如有不明白意思，请进入A VO问题中心解答目录一、A VO 调查问题 (2)二、常识性问题 (3)三、关于A VO账户 (4)四、关于入金 (6)五、关于提款 (7)六、关于推广计划 (8)AVO注册：https:///doc/2f7245444.html/?ref=AVO1796334联系QQ：2454711798一、A VO 调查问题1．1、AVO 是什么？AVO公司是位于美国的一家大型资产管理公司，成立于1996年，2003年注册公司。

AVO INC股份有限公司的有一批专业贸易商、合格的专家、熟练的分析师和经验丰富的银行家。

1．2、AVO的主要投资活动？我们所从事的专业的多元化领域，如股票，债券，商品，货币，能源，原材料及互惠基金。

1．3、AVO从事行业多久？AVO公司成立于1996年，并成为2003年注册成立。

1．4、你的主要优势是什么？首先，我们不会随意借用你的钱。

我们对待我们的投资者，就像我们自己，我们保证您的初始投资和利润都会得到最大利益化，而且都会把你的初始本金返还的。

1．5、AVO值得相信吗？AVO INC国际资产管理公司股份有限公司成为于2003年5月07，在美国纽约注册成立。

你可以到纽约企业黄页查看注册状态。

VeriSign、COMODO和Thawte扩展验证证书（Green-bar SSL）保证AVO公司身份安全和您的交易安全保证，保证金为1500000美元并无限补发。

确信的是，Trustwave公司和ClickID公司为AVO INC 公司信息提供保证，AVO INC公司拥有独立的地址、电话号码、经理、员工、资格和财务报告。

COMODO的防黑客、McAfee安全和GeoTrust的反恶意软件保护，AVO INC在美国和欧洲的数据中心最强大的专用服务器，每小时进行恶意软件和漏洞扫描。

为AVO INC提供全球Web服务器保护的STAMINUS公司是世界上最大和最值得信赖的DDoS保护和解决供应商。

近似支持向量机的AVO类型判别

近似支持向量机的AVO类型判别李文秀;文晓涛;李天;李雷豪;刘松鸣;杨吉鑫【摘要】AVO技术是储层含油气性分析的重要工具,可以定性地描述油气藏.常规储层的AVO分类主要依靠人为判别,致使判别结果不准且工作量大.本文从四类AVO曲线中提取特征参数作为训练集,把近似支持向量机方法引入AVO类型判别;再以四类含气砂岩AVO曲线形态为依据,把叠前地震资料的曲线形态特征作为输入参数,获得工区内储层的AVO类型.将该方法应用于南海碎屑岩气田的AVO类型自动识别,取得了较准确的结果,为储层的AVO类型判别提供了可靠、高效、便捷的工具.【期刊名称】《石油地球物理勘探》【年(卷),期】2018(053)005【总页数】6页(P969-974)【关键词】近似支持向量机;AVO类型;分类;储层分析【作者】李文秀;文晓涛;李天;李雷豪;刘松鸣;杨吉鑫【作者单位】成都理工大学地球物理学院,四川成都610059;成都理工大学油气藏地质及开发工程国家重点实验室,四川成都610059;云南建投第一勘察设计有限公司,云南昆明650031;成都理工大学地球物理学院,四川成都610059;成都理工大学油气藏地质及开发工程国家重点实验室,四川成都610059;云南建投第一勘察设计有限公司,云南昆明650031;成都理工大学地球物理学院,四川成都610059;成都理工大学地球物理学院,四川成都610059;成都理工大学地球物理学院,四川成都610059【正文语种】中文【中图分类】P6311 引言AVO技术研究并利用地震波振幅与炮检距的关系进行油气预测，对于储层含油气分析有着十分重要的意义。

因含气砂岩压实程度差异，与上覆盖层形成不同的物性参数组合，其反射系数随入射角的变化特征也不同。

目前，主要有四类含气砂岩的AVO特征曲线，前三类由Rutherford等[1]提出，Castagna等[2]补充了第Ⅳ类。

其中第Ⅱ类和第Ⅲ类AVO异常是烃类检测的有效标志，通常在叠加剖面上形成“暗点”或“亮点”异常。

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simultaneously P-wave velocity Vp,
S-wave velocity Vs and density ρ
from pre-stack data using either
angle stacks or all traces.
Objectives (2)
Vp1 ,Vs1 , 1
Simulated Annealing
Preliminary Results: Forward
Modeling
Preliminary Results: Method 2
Preliminary Results: Method 2
Preliminary Results: Method 2
Preliminary Results: Method 2
Inversion Algorithms
• Generalized linear inversion (GLI) Gauss-Newton, Conjugate gradient, Least Squares, etc. • Computer learning algorithm Neural network, Support vector machines. • Global optimization Simulated Annealing, Genetic Algorithm, etc.

HORUS

IFP/ Vanguard(SSI)

Vanguard

HR sof t ware

EI

LITHANN

STRATA (+ EI)

ISM AP (2D)

EM ERGE

3-t erm

FWPI
(poststack)
Earthw orks eSeis Flam ingo Fusion Geards (1980)
R( ) A + Bsin 2 Csin 2tan2 V p 1 A 2 V p 1 V p 2 Vs B 2 2 V 2 Vp s 1 C 2 Vs Vp
Deterministic
Stochastic
Neural Networks
PDF Mapping
Full Waveform

EI

St rat aVista

R&D/ Pet roSI?

EI

GDI

2 or 3-t erm

Rockt race

St at mod

Int erwell

ISIS
Vp 2 ,Vs 2 , 2
Vp3 ,Vs 3 , 3
P-wave velocity S-wave velocity density
Synthetic seismic trace
Current RockRes - Data-driven
• Use angle stacks or all traces to invert Vp, Vs, and reflectivities - A&R’s or Fatti’s for reflectivities • Integrate the reflectivities to obtain relative values, i.e, high frequency component – calibrate with wells • Build low frequency model from logs and seismic velocity – calibrate with wells • Add high and low components together – calibrate with wells
• Port the code to ANSER system
• Test with the field data • Port the code to Diamond system
Wavelet extraction and Macro model
• These tasks can be done by RSTigress integration.
Methods
• Use Aki and Richards (1980) or Fatti et al (1994) (3-term) formulations to compute angle dependent reflectivity coefficients. • Use convolution theory to compute synthetic seismic responses. • Use Simulated Annealing (SA) algorithm to inverse Vp, S-wave velocity Vs and density ρ simultaneously.
Seismic Attributes/Logs/Wavelet/Model Rock/Fluid Properties Attributes/Logs Rock/Fluid AVO/AVA Trends Reflectivities( ) Properties Rock/Fluid Properties Petroelastic Modeling/PDF; ’s or Approximations Self Organizing Map, Multi-layer Perceptron, Radial Basis Function, etc. Geostatistics; Full Zoeppritz ’ Zoeppritz s equations ’s Zoeppritz or or Approximations Approximations Aki & Richards Approximation or Variations Simulated Annealing, Monte Carlo, etc. Generalized Delta Rule w/steepest decent, Local Minimum, etc. Sequential Annealing Gaussian Simulation, Simulated Annealing, Monte Carlo, etc. Curve Fitting, Matrix Inversion, Conjugate Gradient, etc.
New Approach - Model-driven
• Method 1: It inverts elastic parameters at each sample from pre-stack amplitude data through sample by sample processing. There are no relationship among samples. • Method 2: Assumes an 1-D multi-layer model and fits the synthetic data and true data to find a group of elastic parameters within each layer from prestack amplitude data.
Convolution Theory
S( , t) R( , )W(t- )d
-
S( , t) - - - angle - dependent seismic t races R( , t) - - angle - dependent reflect ivi ty coefficien ts W(t )- - - - seismic wavelet
Model-based Pre-stack Inversion
Wei Yang
October 23 2008
Outline
• Objectives
• History
• Methods • Preliminary Results
• Plan
Objectives (1)
Develop a software to inverse
Prestack Inversion Competitors
Company
Geotrace CGG dGB Fairfield Fugro/Jason IFP Odegaard Paradigm PGS RSI Veritas/HR WesternGeco
Probe (+ EI)
AVO
RockRes/M axRes
Prestack Seismic Attributes Quantitative Rock/Fluid Properties
Building Blocks (Engines/Algorithms)
AVO Inversion Deterministic Stochastic Neural Networks Probability Density Function Mapping Full Waveform

(poststack)

(Lit hSeis)

(SINV)
Preliminary Results: Method 2
Preliminary Results: Method 2
Transfering the inversion result of Ip and Is into Rp and Rs, I got the following table:
No. Rp true 1 2 3 4 5 -0.1729 0.1729 -0.1729 0.1729 -0.1729 Rp inv -0.1726 0.17331 -0.1724 0.17300 -0.17208 Error % Rs true 0.17 0.23 0.17 0.06 0.47 -0.06005 0.06005 -0.06005 0.06005 -0.06005 Rs inv -0.0699 0.0723 -0.0675 0.0710 -0.0677 Error % 16.4 20.0 11.6 18.3 11.6