地震密度反演及地层孔隙度估计

地层密度直接与孔隙度、孔隙流体类型、饱和度和骨架矿物成分有关.本文通过理论分析和计算,讨论了油气藏储层物性参数变化引起的密度变化及密度变化对地震波速度、阻抗和振幅的影响,提出了基于完全纵波方程的全波形地震密度反演和孔隙度估计方法,克服了常规地震密度反演对地震数据更多处理引起的信号畸变,提高了地震密度反演和地层孔隙度估计的精度.该方法采用波场导数的时间积分和多炮求和,对地震数据中的噪声具有比较强的压制作用.理论模型研究表明该方法是可行的.通过对我国西部某气田实际数据处理、分析和反演,获得了地层密度和孔隙度,结果与测井基本吻合,证明了预测结果的准确性和方法的有效性,从而为后续的有效储层预测和储量计算提供了可靠的数据.     

岩石的临界孔隙度反演及横波预测(英文)

临界孔隙度模型是利用岩石的临界孔隙度来计算岩石骨架的弹性模量,岩石的临界孔隙度值受到很多因素的影响,而实际应用中通常无法获得准确的临界孔隙度值,只能选取经验临界孔隙度值,就会给岩石物理建模带来误差。本文提出了一种利用纵波速度反演岩石的临界孔隙度的方法,并且把它应用于横波预测中。实验室和测井数据应用结果表明本文提出的方法可以降低以往选取经验值带来的不确定性,并且能够为横波预测提供准确的临界孔隙度值,提高了横波预测的精度。     

Determination of porosity and saturation using seismic waveform inversion

Characterization of a reservoir model requires determination of its petrophysical parameters, such as porosity and saturation. We propose a new method to determine these parameters directly from seismic data. The method consists of the computation and inversion of seismic waveforms. A high frequency method is presented to model wave propagation through an attenuative and dispersive poroelastic medium. The high frequency approximation makes it possible to efficiently compute sensitivity functions. This enables the inversion of seismic waveforms for porosity and saturation. The waveform inversion algorithm is applied to two laboratory crosswell datasets of a water saturated sand. The starting models were obtained using travel time tomography. The first dataset is inverted for porosity. The misfit reduction for this dataset is approximately 50%. The second dataset was obtained after injection of a nonaqueous-phase liquid (NAPL), possibly with some air, which made the medium more heterogeneous. This dataset was inverted for NAPL and air saturation using the porosity model obtained from the first inversion. The misfit reduction of the second experiment was 70%. Regions of high NAPL and high air saturation were found at the same location. These areas correlate well with the position of one of the injection points as well as regions of higher NAPL concentrations found after excavation of the sand. It is therefore possible to directly invert waveforms for pore fluid saturation by taking into account the attenuation and dispersion caused by the poroelasticity.     

时间推移地震反演的连续模型与算法

针对时间推移地震反演问题建立了依赖勘探时间的数学模型,并在此基础上设计了全局收敛的连续型及逐次递归算法.将同伦方法与Tikhonov正则化方法相结合,设计了初次勘探反演的同伦-正则化方法.以三维时间推移地震为例,在连续型及逐次递归算法的基础上构造了快速的局域化反演算法.数值模拟结果说明了上述方法都是有效的.     

Rock physics modelling and inversion for saturation-pressure changes in time-lapse seismic studies

Time-lapse seismic data are generally used to monitor the changes in dynamic reservoir properties such as fluid saturation and pore or effective pressure. Changes in saturation and pressure due to hydrocarbon production usually cause changes in the seismic velocities and as a consequence changes in seismic amplitudes and travel times. This work proposes a new rock physics model to describe the relation between saturation-pressure changes and seismic changes and a probabilistic workflow to quantify the changes in saturation and pressure from time-lapse seismic changes. In the first part of this work, we propose a new quadratic approximation of the rock physics model. The novelty of the proposed formulation is that the coefficients of the model parameters (i.e. the saturation-pressure changes) are functions of the porosity, initial saturation and initial pressure. The improvements in the results of the forward model are shown through some illustrative examples. In the second part of the work, we present a Bayesian inversion approach for saturation-pressure 4D inversion in which we adopt the new formulation of the rock physics approximation. The inversion results are validated using synthetic pseudo-logs and a 3D reservoir model for CO₂ sequestration.     

On seismic inversion

Modeling with optimization in waveimpedance inversion (by Xia Hongrui) Xia Hongrui, et. al., pointed out that wave impedanceinversion from seismic data is generally done by solvingequations with two unknowns. However, ill-posed equa-tions cause ill-posed solutions. To overcome this problem,firstly, an initial model is established according to the seis-mic data and well-logging. It is then regarded as the ini-tial solution. The equations are solved by solutionoptimization. In this perspective…     

岩石物理驱动的储层裂缝参数与物性参数概率地震反演方法

长波长假设条件下,各向同性背景地层中发育一组平行排列的垂直裂缝可等效为具有水平对称轴的横向各向同性（HTI）介质.基于不同观测方位的岩石地震响应特征变化,宽方位地震数据不仅可实现裂缝岩石弹性参数与各向异性参数的预测,同时也蕴含着丰富的孔隙度等储层物性参数信息.本文结合实际地震资料提出了贝叶斯框架下岩石物理驱动的储层裂缝参数与物性参数概率地震联合反演方法,首先基于AVAZ反演裂缝岩石的弹性参数与各向异性参数,并在此基础上通过统计岩石物理模型表征孔隙度、裂缝密度等各向异性介质储层参数与裂缝岩石参数的相互关联,并采用马尔科夫链蒙特卡洛（MCMC）抽样方法进行大量样本的随机模拟,使用期望最大化（EM）算法估计后验条件概率分布,最终寻找最大后验条件概率对应的孔隙度、裂缝密度等HTI裂缝介质储层参数即为反演结果.测井及实际地震数据处理表明,该方法能够稳定合理地从方位地震资料中获取裂缝岩石弹性参数与各向异性参数,并提供了一种较为可靠的孔隙度、裂缝密度等裂缝介质储层参数概率地震反演方法.

     

随机不动点地震反演方法

地震反演可以通过地表观测数据来推测地下介质模型参数,因此地震反演技术自提出至今都是储层预测最常用的手段之一.不动点地震反演方法（FPSI）通过将求解目标泛函转换为寻找目标泛函不动点来获得准确与高分辨率的反演结果.由于FPSI对地震信号的振幅变化具有较高的敏感性,因此即使地震剖面存在部分弱反射区域,利用FPSI仍然可以获得较为准确的反演结果.然而FPSI存在抗噪能力不足以及多解性强等问题.为提高FPSI的抗噪能力及实用性,本研究将随机反演框架引入至不动点反演理论,提出随机不动点地震反演方法（SFPSI）.此外,利用纵波阻抗的初始模型计算得到初始反射系数,并将初始反射系数与初始模型约束引入反演框架及目标泛函,有效提高了反演方法的鲁棒性及计算效率.具体地,首先对以待反演参数初始模型为均值的高斯分布进行随机抽样并利用不动点理论判定抽样结果是否被接受.在此基础上对反演参数初始模型进行更新,并利用更新后的反演结果进行下一轮抽样与判定,直至残差小于阈值或迭代次数大于设定值.因此,SFPSI通过反复执行"随机过程"与"不动点修正"来使得初始模型逐渐逼近真实解,并最终收敛于目标泛函的不动点.由于本方法具有一定的随机性,因此需要进行多次反演并以多次反演结果的均值作为最终反演结果.最后,模型以及实际资料测试结果表明本方法有效的提高了FPSI抗噪能力,且本方法的反演结果较FPSI的反演结果具有更高的地层分辨率,并保持了良好的横向连续性.     

Implications of stress-drop models of earthquakes for the inversion of stress drop from seismic observations

We discuss the inversion of stress drops from seismic observations on the basis of crack or stress-drop models of earthquake mechanism. Since a formal inverse problem cannot be posed at present we discuss implications of solutions to direct problems. We first discuss the static approximations used to obtain stress drop from seismic moment and source dimensions. We show that the geometrical effects are quite significant if only one source dimension has been retrieved from seismic observations. The effect of variable stress drop is discussed and we show that the inverted stress drop is not a simple average of the actual stress drops on the fault. We discuss the energy release during faulting and show that the apparent stress has a complicated relation to the stress drop on the fault. We also show that the static stress drops obtained by seismologists are a lower bound to the actual dynamic stress drops on the fault. This may in part explain disagreements with laboratory results. Finally, we discuss the inversion of source dimensions from the far-field radiation. We analyse two extreme, simple dynamical source models, a circular fault and a rectangular fault and show that geometry has a much more pronounced effect on radiation than is usually acknowledged.     

基于波动方程的叠前地震反演

随着地震勘探和开发的不断深入,面向地质目标的精细储层预测技术变得越来越重要.由于透射损失、层间多次波、波模式转换以及随机噪声等的影响,观测地震数据和待反演的地下介质属性之间呈现出很强的非线性.考虑到这些非线性,本文基于积分波动方程开展叠前地震反演,从观测地震数据中恢复出介质属性和整体波场,其中反演参数是波动方程中的压缩系数、剪切柔度和密度的对比度,相比于常规线性AVO反演的波阻抗弹性参数,它们对流体指示有更强的敏感性.在反演过程中,从平滑的低频背景场出发,交替迭代求解数据方程和目标方程.采用乘性正则化方法于共轭梯度框架下求解反演参数,采用优化的散射级数Neumann序列获得整体波场,这种方法不易陷入局部极值,能收敛到正确解.测井资料和典型山前带模型测试表明,利用上述反演方法能获得高分辨率的深度域地下介质属性,可直接进行储层预测和解释.

     

各向异性介质中的弹性阻抗及其反演

地震反演已成为油藏描述中的重要组成部分.绝大多数的常规地震反演是叠后地震数据体上进行,很少考虑各向异性存在的情况.随着勘探开发的发展,地震各向异性和叠前地震波阻抗反演引起了人们极大关注.本文在各向同性介质中弹性阻抗研究基础上,推导出了各向异性介质中的弹性阻抗方程,提出了地震各向异性介质中用弹性阻抗进行储层参数描述的技术路线和框架,并对反演过程中存在的问题进行了有益探讨.     

地震逆散射偏移与反演综述

随着油气勘探领域逐渐向深层、复杂型、隐蔽性油气藏转移,油气资源的勘探难度越来越大,传统反射地震勘探技术难以满足日益增长的油气勘探需求,亟需发展适合复杂地质构造的地震波偏移反演新技术.针对地球深部非均匀结构体引起的地震散射波,发展地震逆散射偏移反演理论和技术将有可能解决复杂构造成像反演的技术难题.本文回顾地震波逆散射偏移反演理论的发展历史和基本原理,以逆广义Radon变换求解线性化逆散射问题为基础,介绍逆散射理论在介质结构成像、物性参数反演、多次波衰减等方面的技术延伸,同时将其应用到合成数据和实际数据资料,探讨地震勘探逆散射方法的技术优势和应用潜力.     

地震包络反演对局部极小值的抑制特性

为了实现包络反演,需要通过一种非线性运算来提取信号包络.这种非线性的包络提取过程可以将信号包络中所包含的对介质扰动的大尺度响应从原始地震信号中分离出来,从而抑制反演中的局部极小值,能够在缺乏低频信息的情况下,为全波形反演提供一个良好的初始模型.本文研究包络反演对局部极小值的抑制作用,并通过目标函数形态的对比来展现这一特性.对Marmousi速度模型和Overthrust速度模型做了反演,证明了该方法的有效性.     

Magnetotelluric data inversion with seismic data constraint

Introduction The research on the structure and physical property of ancient hidden hill, igneous rocks and basement is relatively difficult by using seismic data only. If we combine seismic data, magneto-telluric (MT) data and geophysical data together, better results can be obtained for the above problem. A number of geophysicists at home and abroad, such as CHEN and WANG (1990), Siri-punvarapor and Egbert (2000) have tried many methods to solve the problem by the inversion of seismic da…     

概率神经网络技术在煤田地震反演中的应用研究

实现从构造勘探向岩性勘探阶段的转变,是煤田地震勘探亟待解决的重要问题。其中,地震反演技术是岩性勘探的一种重要手段。为了规避常规反演方法的固有限制,利用概率神经网络技术预测井数据和地震数据之间的非线性关系,得到密度数据体和速度数据体,并获得相应的波阻抗数据体。对某矿区的实际地震资料采用该技术进行岩性反演,得到了较为准确的波阻抗数据体,为岩性解释提供了不可或缺的资料。     

Multi-trace basis-pursuit seismic inversion for resolution enhancement

Seismic inversion is an important tool that transfers interface information of seismic data to formation information, which renders the seismic data easily understood by geologists or petroleum engineers. In this study, a novel multi-trace basis-pursuit inversion method based on the Bayesian theory is proposed to enhance the vertical resolution and overcome the lateral instability of inversion results between different traces occasionally seen in the traditional trace-by-trace basis-pursuit inversion method. The Markov process is initially introduced to describe the relationship between adjacent seismic traces and their correlation, which we then close couple in the equation of our new inversion method. A recursive function is further derived to simplify the inversion process by considering the particularity of the coefficient matrix in the multi-trace inversion equation. A series of numerical-analysis and field data examples demonstrates that both the traditional and the new methods for P-wave impedance inversion are helpful in enhancing the resolution of thin beds that are usually difficult to discern from original seismic profiles, thus highlighting the importance of acoustic-impedance inversion for thin bed interpretation. Furthermore, in addition to yielding thin bed inversion results with enhanced lateral continuity and high vertical resolution, our proposed method is robust to noise and cannot be easily contaminated by it, which we verify using both synthetic and field data.     

基于神经网络的随机地震反演方法

针对随机地震反演中存在的两个主要问题,随机实现含有噪声和难以从大量随机实现中挖掘有效信息,提出了一种基于神经网络的随机地震反演方法.通过对多组随机实现及其正演地震数据的计算,构建了基于序贯高斯模拟的训练集.这也为应用神经网络求解地球物理反问题,提供了一种有效建立训练集的方法.较之传统的神经网络反演,这种训练集不仅保证了学习样本具有多样性,同时还引入了空间相关性.数值模拟结果表明,该方法只需要通过单层前馈神经网络,就可以比较有效的解决一个500个阻抗参数的反演问题.     

Assessing uncertainty in refraction seismic traveltime inversion using a global inversion strategy

To analyse and invert refraction seismic travel time data, different approaches and techniques have been proposed. One common approach is to invert first‐break travel times employing local optimization approaches. However, these approaches result in a single velocity model, and it is difficult to assess the quality and to quantify uncertainties and non‐uniqueness of the found solution. To address these problems, we propose an inversion strategy relying on a global optimization approach known as particle swarm optimization. With this approach we generate an ensemble of acceptable velocity models, i.e., models explaining our data equally well. We test and evaluate our approach using synthetic seismic travel times and field data collected across a creeping hillslope in the Austrian Alps. Our synthetic study mimics a layered near‐surface environment, including a sharp velocity increase with depth and complex refractor topography. Analysing the generated ensemble of acceptable solutions using different statistical measures demonstrates that our inversion strategy is able to reconstruct the input velocity model, including reasonable, quantitative estimates of uncertainty. Our field data set is inverted, employing the same strategy, and we further compare our results with the velocity model obtained by a standard local optimization approach and the information from a nearby borehole. This comparison shows that both inversion strategies result in geologically reasonable models (in agreement with the borehole information). However, analysing the model variability of the ensemble generated using our global approach indicates that the result of the local optimization approach is part of this model ensemble. Our results show the benefit of employing a global inversion strategy to generate near‐surface velocity models from refraction seismic data sets, especially in cases where no detailed a priori information regarding subsurface structures and velocity variations is available.     

Exploring seismic inversion methodologies for non‐stationary geological environments: a benchmark study between deterministic and geostatistical seismic inversion

This paper presents a comparison between subsurface impedance models derived from different deterministic and geostatistical seismic inversion methodologies applied to a challenging synthetic dataset. Geostatistical seismic inversion methodologies nowadays are common place in both industry and academia, contrasting with traditional deterministic seismic inversion methodologies that are becoming less used as part of the geo‐modelling workflow. While the first set of techniques allows the simultaneous inference of the best‐fit inverse model along with the spatial uncertainty of the subsurface elastic property of interest, the second family of inverse methodology has proven results in correctly predicting the subsurface elastic properties of interest with comparatively less computational cost. We present herein the results of a benchmark study performed over a realistic three‐dimensional non‐stationary synthetic dataset in order to assess the performance and convergence of different deterministic and geostatistical seismic inverse methodologies. We also compare and discuss the impact of the inversion parameterisation over the exploration of the model parameter space. The results show that the chosen seismic inversion methodology should always be dependent on the type and quantity of the available data, both seismic and well‐log, and the complexity of the geological environment versus the assumptions behind each inversion technique. The assessment of the model parameter space shows that the initial guess of traditional deterministic seismic inversion methodologies is of high importance since it will determine the location of the best‐fit inverse solution.     

随机地震反演关键参数优选和效果分析(英文)

随机地震反演技术是将地质统计理论和地震反演相结合的反演方法,它将地震资料、测井资料和地质统计学信息融合为地下模型的后验概率分布,利用马尔科夫链蒙特卡洛(MCMC)方法对该后验概率分布采样,通过综合分析多个采样结果来研究后验概率分布的性质,进而认识地下情况。本文首先介绍了随机地震反演的原理,然后对影响随机地震反演效果的四个关键参数,即地震资料信噪比、变差函数、后验概率分布的样本个数和井网密度进行分析并给出其优化原则。资料分析表明地震资料信噪比控制地震资料和地质统计规律对反演结果的约束程度,变差函数影响反演结果的平滑程度,后验概率分布的样本个数决定样本统计特征的可靠性,而参与反演的井网密度则影响反演的不确定性。最后通过对比试验工区随机地震反演和基于模型的确定性地震反演结果,指出随机地震反演可以给出更符合地下实际情况的模型。