The applicability and underlying factors of frequency-dependent amplitude-versus-offset(AVO)inversion

Recently,the great potential of seismic dispersion attributes in oil and gas exploration has attracted extensive attention.The frequency-dependent amplitude versus offset(FAVO)technology,with dispersion gradient as a hydrocarbon indicator,has developed rapidly.Based on the classical AVO theory,the technology works on the assumption that elastic parameters are frequency-dependent,and implements FAVO inversion using spectral decomposition methods,so that it can take dispersive effects into account and effectively overcome the limitations of the classical AVO.However,the factors that affect FAVO are complicated.To this end,we construct a unified equation for FAVO inversion by combining several Zoeppritz approximations.We study and compare two strategies respectively with(strategy 1)and without(strategy 2)velocity as inversion input data.Using theoretical models,we investigate the influence of various factors,such as the Zoeppritz approximation used,P-and S-wave velocity dispersion,inversion input data,the strong reflection caused by non-reservoir interfaces,and the noise level of the seismic data.Our results show that FAVO inversion based on different Zoeppritz approximations gives similar results.In addition,the inversion results of strategy 2 are generally equivalent to that of strategy 1,which means that strategy 2 can be used to obtain dispersion attributes even if the velocity is not available.We also found that the existence of non-reservoir strong reflection interface may cause significant false dispersion.Therefore,logging,geological,and other relevant data should be fully used to prevent this undesirable consequence.Both the P-and S-wave related dispersion obtained from FAVO can be used as good indicators of a hydrocarbon reservoir,but the P-wave dispersion is more reliable.In fact,due to the mutual coupling of P-and S-wave dispersion terms,the P-wave dispersion gradient inverted from PP reflection seismic data has a stronger hydrocarbon detection ability than the S-wave dispersion gradient.Moreover,there is little difference in using post-stack data or pre-stack angle gathers as inversion input when only the P-wave dispersion is desired.The real application examples further demonstrate that dispersion attributes can not only indicate the location of a hydrocarbon reservoir,but also,to a certain extent,reveal the physical properties of reservoirs.

Direct hydrocarbon identification in shale oil reservoirs using fluid dispersion attribute based on an extended frequency-dependent seismic inversion scheme

The identification of hydrocarbons using seismic methods is critical in the prediction of shale oil res-ervoirs.However,delineating shales of high oil saturation is challenging owing to the similarity in the elastic properties of oil-and water-bearing shales.The complexity of the organic matter properties associated with kerogen and hydrocarbon further complicates the characterization of shale oil reservoirs using seismic methods.Nevertheless,the inelastic shale properties associated with oil saturation can enable the utilization of velocity dispersion for hydrocarbon identification in shales.In this study,a seismic inversion scheme based on the fluid dispersion attribute was proposed for the estimation of hydrocarbon enrichment.In the proposed approach,the conventional frequency-dependent inversion scheme was extended by incorporating the PP-wave reflection coefficient presented in terms of the effective fluid bulk modulus.A rock physics model for shale oil reservoirs was constructed to describe the relationship between hydrocarbon saturation and shale inelasticity.According to the modeling results,the hydrocarbon sensitivity of the frequency-dependent effective fluid bulk modulus is superior to the traditional compressional wave velocity dispersion of shales.Quantitative analysis of the inversion re-sults based on synthetics also reveals that the proposed approach identifies the oil saturation and related hydrocarbon enrichment better than the above-mentioned conventional approach.Meanwhile,in real data applications,actual drilling results validate the superiority of the proposed fluid dispersion attribute as a useful hydrocarbon indicator in shale oil reservoirs.

Fluid identification and effective fracture prediction based on frequency-dependent AVOAz inversion for fractured reservoirs

Fluid and effective fracture identification in reservoirs is a crucial part of reservoir prediction.The frequency-dependent AVO inversion algorithms have proven to be effective for identifying fluid through its dispersion property.However,the conventional frequency-dependent AVO inversion algorithms based on Smith&Gidlow and Aki&Richards approximations do not consider the acquisition azimuth of seismic data and neglect the effect of seismic anisotropic dispersion in the actual medium.The aligned fractures in the subsurface medium induce anisotropy.The seismic anisotropy should be considered while accounting for the seismic dispersion properties through fluid-saturated fractured reservoirs.Anisotropy in such reservoirs is frequency-related due to wave-induced fluid-flow(WIFF)between interconnected fractures and pores.It can be used to identify fluid and effective fractures(fluid-saturated)by using azimuthal seismic data via anisotropic dispersion properties.In this paper,based on Rüger’s equation,we derived an analytical expression in the frequency domain for the frequencydependent AVOAz inversion in terms of fracture orientation,dispersion gradient of isotropic background rock,anisotropic dispersion gradient,and the dispersion at a normal incident angle.The frequency-dependent AVOAz equation utilizes azimuthal seismic data and considers the effect of both isotropic and anisotropic dispersion.Reassigned Gabor Transform(RGT)is used to achieve highresolution frequency division data.We then propose the frequency-dependent AVOAz inversion method to identify fluid and characterize effective fractures in fractured porous reservoirs.Through application to high-qualified seismic data of dolomite and carbonate reservoirs,the results show that the method is useful for identifying fluid and effective fractures in fluid-saturated fractured rocks.

Fluid discrimination incorporating viscoelasticity and frequency-dependent amplitude variation with offsets inversion

Frequency-dependent amplitude versus offset(FAVO)inversion is a popular method to estimate the frequency-dependent elastic parameters by using amplitude and frequency information of pre-stack seismic data to guide fluid identification.Current frequency-dependent AVO inversion methods are mainly based on elastic theory without the consideration of the viscoelasticity of oil/gas.A fluid discrimination approach is proposed in this study by incorporating the viscoelasticity and relevant FAVO inversion.Based on viscoelastic and rock physics theories,a frequency-dependent viscoelastic solid-liquid decoupling fluid factor is initially constructed,and its sensitivity in fluid discrimination is compared with other conventional fluid factors.Furthermore,a novel reflectivity equation is derived in terms of the viscoelastic solid-liquid decoupling fluid factor.Due to the introduction of viscoelastic theory,the proposed reflectivity is related to frequency,which is more widely applicable than the traditional elastic reflectivity equation directly derived from the elastic reflectivity equation on frequency.Finally,a pragmatic frequency-dependent inversion method is introduced to verify the feasibility of the equation for frequency-dependent viscoelastic solid-liquid decoupling fluid factor prediction.Synthetic and field data examples demonstrate the feasibility and stability of the proposed approach in fluid discrimination.

Nonlinear joint PP-PS AVO inversion based on improved Bayesian inference and LSSVM

Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equations for multiple iterations. Therefore the inversion results of P-wave, S-wave velocity and density exhibit low precision in the faroffset;thus, the joint PP–PS AVO inversion is nonlinear. Herein, we propose a nonlinear joint inversion method based on exact Zoeppritz equations that combines improved Bayesian inference and a least squares support vector machine (LSSVM) to solve the nonlinear inversion problem. The initial parameters of Bayesian inference are optimized via particle swarm optimization (PSO). In improved Bayesian inference, the optimal parameter of the LSSVM is obtained by maximizing the posterior probability of the hyperparameters, thus improving the learning and generalization abilities of LSSVM. Then, an optimal nonlinear LSSVM model that defi nes the relationship between seismic refl ection amplitude and elastic parameters is established to improve the precision of the joint PP–PS AVO inversion. Further, the nonlinear problem of joint inversion can be solved through a single training of the nonlinear inversion model. The results of the synthetic data suggest that the precision of the estimated parameters is higher than that obtained via Bayesian linear inversion with PP-wave data and via approximations of the Zoeppritz equations. In addition, results using synthetic data with added noise show that the proposed method has superior anti-noising properties. Real-world application shows the feasibility and superiority of the proposed method, as compared with Bayesian linear inversion.

Joint PP and PS AVO inversion based on Zoeppritz equations

Considering Zoeppritz equations, reflections of PP and PS are only the function of ratios of density and velocity. So the inversion results will be the same if the ratios are the same but values of density, velocities of P- wave and S-wave are different without strict constraint. This paper makes efforts to explore nonlinear simultaneous PP and PS inversion with expectation to reduce the ambiguity of AVO analysis by utilizing the redundancy of multi-component AVO measurements. Accurate estimation of ratio parameters depends on independence of input data. There are only two independent AVO attributes for PP reflectivity （i.e. intercept and gradient） and two for PS reflectivity （i.e. pseudo-intercept and pseudo-gradient or extreme amplitude）, respectively. For individual PP and PS inversion, the values of least-squares objective function do not converge around a large neighborhood of chosen true model parameters. Fortunately for joint PP and PS inversion the values of the least-squares objective function show closed contours with single minima. Finally the power function fitting is used to provide a higher precision AVO attributes than traditional polynomial fitting. By using the four independent fitting attributes （two independent attributes for PP and PS respectively）, the inversion of four ratio parameters （velocities and densities） would be estimated with less errors than that in traditional method.

Seismic AVO statistical inversion incorporating poroelasticity

Seismic amplitude variation with offset(AVO) inversion is an important approach for quantitative prediction of rock elasticity,lithology and fluid properties.With Biot-Gassmann's poroelasticity,an improved statistical AVO inversion approach is proposed.To distinguish the influence of rock porosity and pore fluid modulus on AVO reflection coefficients,the AVO equation of reflection coefficients parameterized by porosity,rock-matrix moduli,density and fluid modulus is initially derived from Gassmann equation and critical porosity model.From the analysis of the influences of model parameters on the proposed AVO equation,rock porosity has the greatest influences,followed by rock-matrix moduli and density,and fluid modulus has the least influences among these model parameters.Furthermore,a statistical AVO stepwise inversion method is implemented to the simultaneous estimation of rock porosity,rock-matrix modulus,density and fluid modulus.Besides,the Laplace probability model and differential evolution,Markov chain Monte Carlo algorithm is utilized for the stochastic simulation within Bayesian framework.Models and field data examples demonstrate that the simultaneous optimizations of multiple Markov chains can achieve the efficient simulation of the posterior probability density distribution of model parameters,which is helpful for the uncertainty analysis of the inversion and sets a theoretical fundament for reservoir characterization and fluid discrimination.

Rock physics and seismic reflectivity parameterization and amplitude variation with offsets inversion in terms of total organic carbon indicator

Total organic carbon (TOC) prediction with elastic parameter inversions has been widely used in the identification and evaluation of source rocks. However, the elastic parameters used to predict TOC are not only determined by TOC but also depend on the other physical properties of source rocks. Besides, the TOC prediction with the elastic parameters inversion is an indirect method based on the statistical relationship obtained from well logs and experiment data. Therefore, we propose a rock physics model and define a TOC indicator mainly affected by TOC to predict TOC directly. The proposed rock physics model makes the equivalent elastic moduli of source rocks parameterized by the TOC indicator. Combining the equivalent elastic moduli of source rocks and Gray’s approximation leads to a novel linearized approximation of the P-wave reflection coefficient incorporating the TOC indicator. Model examples illustrate that the novel reflectivity approximation well agrees with the exact Zoeppritz equation until incident angles reach 40°. Convoluting the novel P-wave reflection approximation with seismic wavelets as the forward solver, an AVO inversion method based on the Bayesian theory is proposed to invert the TOC indicator with seismic data. The synthetic examples and field tests validate the feasibility and stability of the proposed AVO inversion approach. Using the inversion results of the TOC indicator, TOC is directly and accurately estimated in the target area.

AVO inversion based on common shot migration

常规 AVO 倒置利用 CMP 集合的踪迹振幅。有三个主要因素，影响倒置的精确性。首先， CMP 集合基于水平层的假设，但是很真实的层不是水平的。处于观察 CMP 集合和他们的真实地点之间的更大的差别的更大的层剧降结果。第二，象 NMO， DMO，和 deconvolution 那样的常规处理流动将弄歪振幅。第三，思考系数的明确的表达与发生角度有关，得到在振幅和发生角度之间的关系是困难的。波浪方程 prestack 深度移植有成像建筑群媒介和陡峭地蘸的层的能力。它能减少回到他们的真实地点的常规处理和行动振幅的错误。与真振幅移植，普通角度集合抽象，和 AVO 倒置，我们从普通射击集合建议 AVO 倒置的一个方法以便减少上述因素的效果并且改进 AVO 倒置的精确性。

Quantification of Reservoir Fluid Prediction Uncertainty Using AVO Fluid Inversion (AFI)

The AVO fluid inversion (AFI) technique was used to assess for fluids at the target levels of OPL-X in the deepwater Niger Delta, Nigeria. In this study, attempt is made to get a quantitative probability estimate of the possible reservoir fluids in both the shallow and deeper target levels. This was achieved through the development of a stochastic AVO model and an inversion to probability of different fluids using the Bayesian approach. AVO Fluid Inversion (AFI) technique provides a robust and inexpensive method for identifying potential hydrocarbon-filled reservoirs and provides a quantitative estimates of the uncertainties inherent in the prediction.

Pre-stack AVO inversion with adaptive edge preserving smooth filter regularization based on Aki-Richard approximation

With the development of exploration of oil and gas resources,the requirements for seismic inversion results are getting more accurate.In particular,it is hoped that the distribution patterns of oil and gas reservoirs can be finely characterized,and the seismic inversion results can clearly characterize the location of stratigraphic boundaries and meet the needs of accurate geological description.Specifically,for pre-stack AVO inversion,it is required to be able to distinguish smaller geological targets in the depth or time domain,and clearly depict the vertical boundaries of the geological objects.In response to the above requirements,we introduce the preprocessing regularization of the adaptive edge-preserving smooth filter into the pre-stack AVO elastic parameter inversion to clearly invert the position of layer boundary and improve the accuracy of the inversion results.

Nonlinear amplitude inversion using a hybrid quantum genetic algorithm and the exact zoeppritz equation

The amplitude versus offset/angle(AVO/AVA)inversion which recovers elastic properties of subsurface media is an essential tool in oil and gas exploration.In general,the exact Zoeppritz equation has a relatively high accuracy in modelling the reflection coefficients.However,amplitude inversion based on it is highly nonlinear,thus,requires nonlinear inversion techniques like the genetic algorithm(GA)which has been widely applied in seismology.The quantum genetic algorithm(QGA)is a variant of the GA that enjoys the advantages of quantum computing,such as qubits and superposition of states.It,however,suffers from limitations in the areas of convergence rate and escaping local minima.To address these shortcomings,in this study,we propose a hybrid quantum genetic algorithm(HQGA)that combines a self-adaptive rotating strategy,and operations of quantum mutation and catastrophe.While the selfadaptive rotating strategy improves the flexibility and efficiency of a quantum rotating gate,the operations of quantum mutation and catastrophe enhance the local and global search abilities,respectively.Using the exact Zoeppritz equation,the HQGA was applied to both synthetic and field seismic data inversion and the results were compared to those of the GA and QGA.A number of the synthetic tests show that the HQGA requires fewer searches to converge to the global solution and the inversion results have generally higher accuracy.The application to field data reveals a good agreement between the inverted parameters and real logs.

叠前AVO反演技术在塔河油田三种类型油气藏流体识别中的适用性分析

塔河油田奥陶系油气藏储层非均质性极强,烃源岩长期生排烃、多期充注成藏及混合改造,导致油气性质变化大,给流体识别带来巨大挑战。通过模型正演,分析缝洞型储层厚度、孔隙度、含流体性质对AVO特征的影响,明确气藏、轻质油藏、重质油藏三种不同类型油气藏的AVO特征及敏感参数;在此基础上,开展叠前反演,获得地下不同流体纵波阻抗及纵横波速度比特征,然后基于实际测井数据,建立三种不同类型油气藏岩石物理量版,在岩石物理量版指导下,利用双参数进行流体概率分析,获得缝洞储层流体定量识别结果。对塔河A区(气藏)、B区(轻质油藏)和C区(重质油藏),各50 km 2三维地震资料开展基于叠前AVO反演的流体识别应用研究,将识别结果用于盲井检验,气藏识别符合率为80%,轻质油藏符合率为76%,重质油藏符合率为72%。研究结果为塔河碳酸盐岩储层流体识别提供了参考依据。

Model-data-driven seismic inversion method based on small sample data

As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this problem,a model-data-driven seismic AVO(amplitude variation with offset)inversion method based on a space-variant objective function has been worked out.In this method,zero delay cross-correlation function and F norm are used to establish objective function.Based on inverse distance weighting theory,change of the objective function is controlled according to the location of the target CDP(common depth point),to change the constraint weights of training samples,initial low-frequency models,and seismic data on the inversion.Hence,the proposed method can get high resolution and high-accuracy velocity and density from inversion of small sample data,and is suitable for identifying thin interbedded sand bodies.Tests with thin interbedded geological models show that the proposed method has high inversion accuracy and resolution for small sample data,and can identify sandstone and mudstone layers of about one-30th of the dominant wavelength thick.Tests on the field data of Lishui sag show that the inversion results of the proposed method have small relative error with well-log data,and can identify thin interbedded sandstone layers of about one-15th of the dominant wavelength thick with small sample data.

基于Xu-White模型的叠前AVO优化反演在深海浊积储层应用

Glengorm区上侏罗统砂层组Freshney油气资源富集,储层精细刻画是其难点,主要包含两方面:(1)目的层段砂、泥岩阻抗叠置,而受制于测井资料品质,叠前Vp/Vs弹性信息辨识岩性能力较弱;(2)地震资料频带窄、信噪比S/N较低,导致常规反演手段难以有效刻画储层,气层预测难度大。对此,本文首先应用Xu-White岩石物理建模方法,正演分析弹性参数并优化Vp/Vs辨识岩性能力;其次,在叠前AVO反演流程上提出统计性子波解决常规反演砂体预测精度较低问题;最后,利用FFP岩相流体识别技术完成Freshney气层精细刻画。靶区应用表明:该项技术较好地解决了Freshney储层流体刻画难等问题,对英国北海盆地深海浊积砂岩精细刻画具有一定的借鉴意义。

准东地区云质岩储层叠前反演及脆性预测

脆性是一种评价致密储层可压裂性的重要指标,对油气勘探与开发有着重要的意义。准东地区勘探程度低、探井少,二叠系中下部云质岩致密储层的精细描述和脆性的准确预测均有着很大的难度。为此,根据岩石物理实验结果,利用叠前地震角度域数据体,开展储层叠前反演及脆性预测。通过AVO反演,获取P(截距)+G(梯度)和P×G属性;通过Y(杨氏模量,E)P(泊松比,σ)D(密度,ρ)和L(拉梅常数,λ)M(μ)R(ρ)反演,获取弹性参数的变化率(反射系数);通过测井约束反演,获取弹性参数的实际值,并用多种表征公式计算脆性,分析其物理含义及应用效果。研究结果表明:①研究区横波速度与纵波速度有较好的拟合关系,杨氏模量、泊松比和密度对泥岩、非泥岩区分度明显;②云质岩具有高速特征,研究区储层呈双“甜点”结构;③用三个小角度数据体直接反演弹性参数反射系数,YPD反演方法相较LMR的储层识别效果更好、分辨率更高,密度反射系数数据体可见比较明显的扇体及河道冲积特征;④脆性表征结果与弹性参数特征有一定的区别,脆性表征的云质岩有利区呈条带状分布,而弹性参数预测的云质岩有利区基本呈连续、成片分布;⑤ρE/σ为研究区最佳的脆性表征公式,可为类似油田云质岩储层脆性预测提供借鉴。

多波时移地震AVO反演研究

数值模拟了油藏含油饱和度与有效压力变化时移地震AVO的响应,确定利用时移地震AVO区分油藏参数的变化、实现油藏定量解释的可行性.从Aki等AVO近似方程出发,详细推导了P_P波和P_S转换波时移地震AVO计算公式.结合岩石物理近似关系和本文推导的时移地震AVO计算公式,推导了利用多波时移地震AVO反演油藏含油饱和度和压力变化的方程.数据试验表明,文中推导的多波时移地震AVO方程能较好地反演油藏含油饱和度变化和有效压力变化,实现油藏定量解释.

粒子滤波非线性AVO反演方法

提出一种新的AVO非线性反演方法,即利用粒子滤波器来求解AVO非线性贝叶斯反演问题。利用随机带权样本逼近后验概率.论文首先论述了粒子滤波器的基本原理,包括状态转移模型与观测模型,权值预测与更新,重要性密度采样等粒子滤波器应用中的关键技术.然后建立了适合于AVO反演的粒子滤波器状态转移模型和观测模型.最后,利用该方法分别对模型数据和实际资料进行了反演计算.反演结果表明,该方法具有较好的稳定性,在AVO反演中具有的一定的应用潜力.同时对地球物理反演的其他问题求解也提供了一条新的途径.

用纵波AVO数据反演储层裂隙密度参数

主要研究以成熟的纵波信息采集和处理技术为基础的反演裂隙型储层特征参数的方法。详细讨论了弹性参数、各向异性系数和裂隙特征参数之间的关系 ,导出了利用AVO数据反演裂隙密度参数的基本方程 ,并给出了二步削减参数反演的流程。数值试验结果表明 。