基于岩石物理的地震储层预测方法应用研究
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摘要
储层预测研究主要在于弄清储层构造特征、岩性特征及储层参数,进而减少勘探开发风险。储层参数包括孔隙度、渗透率、流体类型等,而地震资料提供的是地震波旅行时和振幅信息,再通过反演可得到弹性参数。地震岩石物理学则为储层参数和弹性参数之间搭建桥梁。本文讨论了不同地质条件下储层物性和流体特征与地震响应特征之间的关系,建立不同尺度岩石物理分析模型,开展了横波速度估算该方法研究、基于岩石物理的AVO正演、地震奇异性属性提取等方面研究,结合地震吸收特征及叠前叠后联合反演技术进行储层岩性预测和流体检测。
横波速度是重要的地球物理参数。在近些年发展起来的叠前地震储层弹性参数反演及流体检测方面起着重要作用。本文深入研究了通过地震岩石物理理模型结合叠前弹性波波形反演精细估算横波速度方法,在方法实现上利用Xu-White模型为初始模型计算岩石模量,该模型是砂泥岩介质模型,考虑了砂泥岩孔隙特性差异及孔隙空间形状,克服了传统等效介质模型的不足;以此为基础,利用测井曲线重构反演方法对模型所需的经验模量参数进行修正,得到较精确的横波速度;再进一步结合地震叠前弹性波波形反演方法对测井曲线重构得到的横波速度进一步修正,最终获得高精度横波速度。整套方法考虑了不同岩性孔隙形状影响,修正了原始模型参数的误差,结合叠前地震数据丰富的振幅和旅行时信息,使横波估算结果更为合理,为进一步研究储层预测和流体识别奠定了技术基础。
AVO分析是利用反射振幅随偏移距变化特征来分析和反演岩性。根据本文提出的横波估算法得到纵横波速度及密度,构建AVO正演模型;利用正演模拟,分析不同地质条件下油气水及特殊岩性体的AVO特征,建立相应检测标志,有助于从实际地震记录中直接识别油气和岩性。正演模拟需解决旅行时与振幅计算两个关键问题。本文基于常速度梯度网格射线追踪法计算旅行时及确定入射角,利用Zoeppritz方程获取振幅值。射线追踪正演模拟方法较适用于纵向非均匀地层,经测试,算法的稳定性及准确性均达到了正演模拟要求。文中利用此方法研究典型AVO油气藏,结合流体替代的正演模拟分析,分析不同流体状态的AVO效应和地震属性的判别模式。
以某实际工区为研究目标,将上述横波速度估算结果分别应用于地震奇异性属性提取和检测弹性阻抗反演中。地震数据既反映地层中岩性的信息(速度和密度),也包含过渡界面上的奇异性信息(反射系数)。本文利用具有较好时频局部化性质的小波变换,通过利普希茨指数刻画信号中奇点位置和大小,能够检测地震信号局部奇异性,利用小波变换系数的模极大值法提取地震奇异性属性,不仅能刻画地质体的外部形态,亦清晰呈现出沉积界面与沉积期次,为进一步精细解释提供了新的信息。叠前弹性阻抗反演充分利用可反映振幅随偏移距变化的角度部分叠加道集进行反演,因而具有良好的保真性和多信息性,由此反演得到的弹性阻抗数据体,由此进一步计算可获得纵波速度、横波速度、密度、纵、横波速度比、泊松比等这些能够反映岩性及流体特征的岩性参数,最终形成丰富的AVO(或AVA)属性,帮助我们更加准确地描述地下储层的信息。
The study of reservoir prediction is mainly to investigate the characteristics of reservoir structure, lithologic features and reservoir parameters, aim to reduce the risk of exploration. Reservoir parameters include porosity, permeability, fluid type, etc., but seismic data only reflects on seismic traveltime, amplitude information, and elastic parameters which can be obtained through seismic inversion. Seismic rock physics builds bridges for reservoir parameters and elastic parameters. This dissertation discusses the relationship between reservoir properties, fluid characteristics and seismic response characteristics under different geological conditions, establishing rock physics analysis models of different scale, carrying out the studies of S-wave velocity estimate method, AVO forward modeling based on rock physics, seismic singularities attributes extraction, etc., in combination with earthquake absorption characteristics and post-stack and pre-stack inversion technologies to perform the prediction of reservoir lithology and fluid detection.
S-wave velocity, an important geophysical parameter, plays an important role in pre-stack seismic reservoir elastic parameter inversion and fluid detection which developed in recent years. In this dissertation, the author delves into the study of meticulous estimate method of S-wave velocity through the seismic rock physics model and pre-stack elastic wave waveform inversion, using Xu-White model as the initial model to calculate rock module. The model is a sandshale medium model, taking into account of the differences of sandshale porosity and pore space shape, overcoming the shortcomings of traditional equivalent medium models. On this basis, well log reconstruction inversion method is employed to make modification to the experience modulus parameters which are necessary for the model; pre-stack seismic waveform inversion method is used to further amend the S-wave velocity from the well log reconstruction to finally get high precision S-wave velocity. The methodology takes into account the impact of different lithological pore shape and corrects the error of the original model parameters, combining rich amplitude and travel time information of pre-stack seismic data, so that the estimated S-wave is more reasonable, laying technical foundation for further research of reservoir prediction and fluid discrimination.
AVO analysis performs the analysis and inversion of the lithology through reflection amplitude variation with offset distance., We get P and S wave velocities and density through the S-wave estimate method put forward in this dissertation, building AVO forward model; and forward modeling is used to analyze AVO characteristics of oil-gas-water and special lithologic bodies under different geological conditions, so as to establish corresponding detection signs, which are useful for oil gas and lithology discrimination from the actual seismic records directly. Traveltime and amplitude calculation are critical for forward modeling. This dissertation calculates traveltime and determines the angle of incidence in the method of ray tracing with constant velocity gradient, using Zoeppritz equation to obtain the amplitude value. Ray tracing forward modeling approach is more applicable to vertical non-uniform strata, and the experimental results show that the stability and accuracy of the algorithm have been up to the requirements of forward modeling. The dissertation studies typical AVO reservoirs in this method, and analyzes AVO effects of different fluids and seismic properties discrimination model, with the forward modeling analysis of fluid substitution.
Through studying certain actual work area, S-wave velocity estimation results above are separately applied to seismic singularity attribute extraction and elastic impedance inversion detection. Seismic data reflects the lithology (velocity and density) as well as seismic singularity attributes on the transition interface. This dissertation gives a description of external morphology of geological bodies as well as a clear presentation of sediment interface and deposition stages in the fans, which provides an effective guarantee for the further precise interpretation and prediction of reservoirs in geologic bodies, by using wavelet transformation characterized by its good time-frequency localization properties, which can depict the location and size of the singular points of the seismic signal through Lipschitz index, to detect the local singularity of seismic signals and using wavelet transformation coefficient modulus maxima method to extract seismic singularity attributes. Pre-stack elastic impedance inversion can be performed by making full use of stacking gathers of the angles which reflect the AVO, so it has high fidelity and much information. Then elastic impedance data derived from the above inversion can be used to obtain the lithologic parameters reflecting the characteristics of lithology and fluid, such as P-wave velocity, shear velocity, density, longitudinal and transversal wave velocity ratio and Poisson’s ratio, etc. by further calculation, and finally rich AVO (or AVA) attributes will be gained to help us describe the underground reservoir more accurately.
横波速度是重要的地球物理参数。在近些年发展起来的叠前地震储层弹性参数反演及流体检测方面起着重要作用。本文深入研究了通过地震岩石物理理模型结合叠前弹性波波形反演精细估算横波速度方法,在方法实现上利用Xu-White模型为初始模型计算岩石模量,该模型是砂泥岩介质模型,考虑了砂泥岩孔隙特性差异及孔隙空间形状,克服了传统等效介质模型的不足;以此为基础,利用测井曲线重构反演方法对模型所需的经验模量参数进行修正,得到较精确的横波速度;再进一步结合地震叠前弹性波波形反演方法对测井曲线重构得到的横波速度进一步修正,最终获得高精度横波速度。整套方法考虑了不同岩性孔隙形状影响,修正了原始模型参数的误差,结合叠前地震数据丰富的振幅和旅行时信息,使横波估算结果更为合理,为进一步研究储层预测和流体识别奠定了技术基础。
AVO分析是利用反射振幅随偏移距变化特征来分析和反演岩性。根据本文提出的横波估算法得到纵横波速度及密度,构建AVO正演模型;利用正演模拟,分析不同地质条件下油气水及特殊岩性体的AVO特征,建立相应检测标志,有助于从实际地震记录中直接识别油气和岩性。正演模拟需解决旅行时与振幅计算两个关键问题。本文基于常速度梯度网格射线追踪法计算旅行时及确定入射角,利用Zoeppritz方程获取振幅值。射线追踪正演模拟方法较适用于纵向非均匀地层,经测试,算法的稳定性及准确性均达到了正演模拟要求。文中利用此方法研究典型AVO油气藏,结合流体替代的正演模拟分析,分析不同流体状态的AVO效应和地震属性的判别模式。
以某实际工区为研究目标,将上述横波速度估算结果分别应用于地震奇异性属性提取和检测弹性阻抗反演中。地震数据既反映地层中岩性的信息(速度和密度),也包含过渡界面上的奇异性信息(反射系数)。本文利用具有较好时频局部化性质的小波变换,通过利普希茨指数刻画信号中奇点位置和大小,能够检测地震信号局部奇异性,利用小波变换系数的模极大值法提取地震奇异性属性,不仅能刻画地质体的外部形态,亦清晰呈现出沉积界面与沉积期次,为进一步精细解释提供了新的信息。叠前弹性阻抗反演充分利用可反映振幅随偏移距变化的角度部分叠加道集进行反演,因而具有良好的保真性和多信息性,由此反演得到的弹性阻抗数据体,由此进一步计算可获得纵波速度、横波速度、密度、纵、横波速度比、泊松比等这些能够反映岩性及流体特征的岩性参数,最终形成丰富的AVO(或AVA)属性,帮助我们更加准确地描述地下储层的信息。
The study of reservoir prediction is mainly to investigate the characteristics of reservoir structure, lithologic features and reservoir parameters, aim to reduce the risk of exploration. Reservoir parameters include porosity, permeability, fluid type, etc., but seismic data only reflects on seismic traveltime, amplitude information, and elastic parameters which can be obtained through seismic inversion. Seismic rock physics builds bridges for reservoir parameters and elastic parameters. This dissertation discusses the relationship between reservoir properties, fluid characteristics and seismic response characteristics under different geological conditions, establishing rock physics analysis models of different scale, carrying out the studies of S-wave velocity estimate method, AVO forward modeling based on rock physics, seismic singularities attributes extraction, etc., in combination with earthquake absorption characteristics and post-stack and pre-stack inversion technologies to perform the prediction of reservoir lithology and fluid detection.
S-wave velocity, an important geophysical parameter, plays an important role in pre-stack seismic reservoir elastic parameter inversion and fluid detection which developed in recent years. In this dissertation, the author delves into the study of meticulous estimate method of S-wave velocity through the seismic rock physics model and pre-stack elastic wave waveform inversion, using Xu-White model as the initial model to calculate rock module. The model is a sandshale medium model, taking into account of the differences of sandshale porosity and pore space shape, overcoming the shortcomings of traditional equivalent medium models. On this basis, well log reconstruction inversion method is employed to make modification to the experience modulus parameters which are necessary for the model; pre-stack seismic waveform inversion method is used to further amend the S-wave velocity from the well log reconstruction to finally get high precision S-wave velocity. The methodology takes into account the impact of different lithological pore shape and corrects the error of the original model parameters, combining rich amplitude and travel time information of pre-stack seismic data, so that the estimated S-wave is more reasonable, laying technical foundation for further research of reservoir prediction and fluid discrimination.
AVO analysis performs the analysis and inversion of the lithology through reflection amplitude variation with offset distance., We get P and S wave velocities and density through the S-wave estimate method put forward in this dissertation, building AVO forward model; and forward modeling is used to analyze AVO characteristics of oil-gas-water and special lithologic bodies under different geological conditions, so as to establish corresponding detection signs, which are useful for oil gas and lithology discrimination from the actual seismic records directly. Traveltime and amplitude calculation are critical for forward modeling. This dissertation calculates traveltime and determines the angle of incidence in the method of ray tracing with constant velocity gradient, using Zoeppritz equation to obtain the amplitude value. Ray tracing forward modeling approach is more applicable to vertical non-uniform strata, and the experimental results show that the stability and accuracy of the algorithm have been up to the requirements of forward modeling. The dissertation studies typical AVO reservoirs in this method, and analyzes AVO effects of different fluids and seismic properties discrimination model, with the forward modeling analysis of fluid substitution.
Through studying certain actual work area, S-wave velocity estimation results above are separately applied to seismic singularity attribute extraction and elastic impedance inversion detection. Seismic data reflects the lithology (velocity and density) as well as seismic singularity attributes on the transition interface. This dissertation gives a description of external morphology of geological bodies as well as a clear presentation of sediment interface and deposition stages in the fans, which provides an effective guarantee for the further precise interpretation and prediction of reservoirs in geologic bodies, by using wavelet transformation characterized by its good time-frequency localization properties, which can depict the location and size of the singular points of the seismic signal through Lipschitz index, to detect the local singularity of seismic signals and using wavelet transformation coefficient modulus maxima method to extract seismic singularity attributes. Pre-stack elastic impedance inversion can be performed by making full use of stacking gathers of the angles which reflect the AVO, so it has high fidelity and much information. Then elastic impedance data derived from the above inversion can be used to obtain the lithologic parameters reflecting the characteristics of lithology and fluid, such as P-wave velocity, shear velocity, density, longitudinal and transversal wave velocity ratio and Poisson’s ratio, etc. by further calculation, and finally rich AVO (or AVA) attributes will be gained to help us describe the underground reservoir more accurately.
引文
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