Characterization of a carbonate reservoir using elastic full-waveform inversion of vertical seismic profile data

Two-dimensional elastic full waveform inversion was applied to two lines extracted from a spiral three-dimensional vertical seismic profile data acquired in an oilfield offshore, Abu Dhabi, in the United Arab Emirates. The lines were selected to be parallel and perpendicular to the plane defined by the deviated borehole. The purpose of the inversion was to derive high-resolution elastic properties of the subsurface. After pre-processing, the data were band-pass filtered with a minimum frequency of 3.5 Hz and a maximum frequency of 30 Hz. A sequential inversion approach was used to mitigate non-linearity. The pre-processing of the data consisted in the removal of bad traces, followed by amplitude and phase corrections. High-resolution P- and S-wave velocity models that show good correlations with the available sonic logs were obtained. The results of the inversion suggest that the oilfield consists of a stack of layers with varying lithology, porosity and possibly fluid content.     

Anisotropic elastic full‐waveform inversion of walkaway vertical seismic profiling data from the Arabian Gulf

Borehole seismic addresses the need for high‐resolution images and elastic parameters of the subsurface. Full‐waveform inversion of vertical seismic profile data is a promising technology with the potential to recover quantitative information about elastic properties of the medium. Full‐waveform inversion has the capability to process the entire wavefield and to address the wave propagation effects contained in the borehole data—multi‐component measurements; anisotropic effects; compressional and shear waves; and transmitted, converted, and reflected waves and multiples. Full‐waveform inversion, therefore, has the potential to provide a more accurate result compared with conventional processing methods. We present a feasibility study with results of the application of high‐frequency (up to 60 Hz) anisotropic elastic full‐waveform inversion to a walkaway vertical seismic profile data from the Arabian Gulf. Full‐waveform inversion has reproduced the majority of the wave events and recovered a geologically plausible layered model with physically meaningful values of the medium.     

基于随机森林算法的陆相沉积烃源岩定量地震刻画:以东海盆地长江坳陷为例

烃源岩的定量地震刻画对于勘探开发区块的优选、盆地油气资源量的估算都具有重要意义.陆相沉积环境下的浅湖或半深湖相的烃源岩横向变化快,其空间展布需要依靠钻井约束下的反射地震进行刻画,但是其地震弹性特征与岩性和有机质含量的映射关系呈现高度非线性化,因而很难利用传统基于地震岩石物理模型驱动的烃源岩地震预测方法进行有效刻画.本文...     

基于频变AVO技术对多尺度裂缝内流体属性反演与识别（英文）

通过地震数据获取裂缝储藏中流体的性质并对流体类型进行识别,是地震勘探岩性反演的重要问题之一。由于地震波的速度、储层的密度等弹性参数对某些流体不具有很强的敏感性,使只依赖振幅信息进行流体识别的传统AVO方法面临困境。作为传统叠前振幅反演的一个拓展,频变AVO（FDAVO）技术进一步考虑了振幅对频率的依赖关系,将这种依赖关系与地下裂缝结构、流体填充对应起来,能带来更丰富的流体信息。利用该技术,本文提出了一种基于地震数据参数化Chapman模型的贝叶斯反演新方法（BIDCMP）,它包含两步算法,即,FDAVO反演储层的非弹性属性和贝叶斯框架下的流体识别。首先,通过匹配观测数据和模型数据,构造差函数反演裂缝储层非弹性参数。随后,在贝叶斯框架下,使用马尔科夫随机场（MRF）作为先验模型,联合多参数场识别流体。本方法在计算过程中,除综合考虑了弹性参数场、测井资料等常规信息外,还特别地加人了第一步中反演得的非弹性参数的约束,从而充分利用了流体粘性差异,最后在最大后验概率（MAP）准则下输出最佳岩性一流体识别结果。分别对合成地震记录和模拟岩性—流体剖面验证本文方法的有效性,结果证明本文方法获得的流体识别结果准确可信。     

利用岩性解释方法圈定岩浆岩侵入煤层范围

岩性地震反演技术是根据钻孔测井数据纵向分辨率很高的有利条件,对井旁地震资料进行约束;并在此基础上对孔间地震资料进行反演,推断煤系地层岩性在平面上的变化情况.谱分解技术是利用薄层调谐体离散频率特性,通过分析复杂岩层内陷频谱变化和局部相位的不稳定性,识别薄地层横向分布特征.本文根据崔庄煤矿三采区的地震资料,综合利用地震反演技术和谱分解技术等岩性解释方法圈定岩浆岩侵入煤层的范围.     

PP and PS joint AVO inversion and fluid prediction

The technique of amplitude variation with offset or angle (AVO or AVA) can be used to extract fluid and lithology information from prestack seismic data. Based on three-term AVO equations, three elastic parameters can be inverted for by linear AVO inversion. However, many theoretical and numerical studies have demonstrated that by using offset limited data, a three-term AVO inversion may have problems of instability and inaccuracy while inverting for the density term. We have searched for an elastic parameter that contains density information and inverted this parameter in a more stable manner using offset limited data. First, we test the sensitivity of elastic parameters to hydrocarbon reservoirs and select the optimal fluid factor (ρf) that contains density information and has an excellent performance as an inversion parameter used to detect hydrocarbons. Then, we derive approximate PP and PS reflection coefficient equations in terms of the fluid factor. The derived equations allow us to directly estimate the fluid factor of the reservoir. Finally, we apply these equations to synthetic data by employing a joint AVO inversion technique. The results show that the method is stable and unambiguous.     

Seismic inversion for underground fractures detection based on effective anisotropy and fluid substitution

Underground fractures play an important role in the storage and movement of hydrocarbon fluid. Fracture rock physics has been the useful bridge between fracture parameters and seismic response. In this paper, we aim to use seismic data to predict subsurface fractures based on rock physics. We begin with the construction of fracture rock physics model. Using the model, we may estimate P-wave velocity, S-wave velocity and fracture rock physics parameters. Then we derive a new approximate formula for the analysis of the relationship between fracture rock physics parameters and seismic response, and we also propose the method which uses seismic data to invert the elastic and rock physics parameters of fractured rock. We end with the method verification, which includes using well-logging data to confirm the reliability of fracture rock physics effective model and utilizing real seismic data to validate the applicability of the inversion method. Tests show that the fracture rock physics effective model may be used to estimate velocities and fracture rock physics parameters reliably, and the inversion method is resultful even when the seismic data is added with random noise. Real data test also indicates the inversion method can be applied into the estimation of the elastic and fracture weaknesses parameters in the target area.     

Facies-constrained prestack seismic probabilistic inversion driven by rock physics

Seismic Rock physics plays a bridge role between the rock moduli and physical properties of the hydrocarbon reservoirs. Prestack seismic inversion is an important method for the quantitative characterization of elasticity, physical properties, lithology and fluid properties of subsurface reservoirs. In this paper, a high order approximation of rock physics model for clastic rocks is established and one seismic AVO reflection equation characterized by the high order approximation(Jacobian and Hessian matrix) of rock moduli is derived. Besides, the contribution of porosity, shale content and fluid saturation to AVO reflectivity is analyzed. The feasibility of the proposed AVO equation is discussed in the direct estimation of rock physical properties. On the basis of this, one probabilistic AVO inversion based on differential evolution-Markov chain Monte Carlo stochastic model is proposed on the premise that the model parameters obey Gaussian mixture probability prior model. The stochastic model has both the global optimization characteristics of the differential evolution algorithm and the uncertainty analysis ability of Markov chain Monte Carlo model. Through the cross parallel of multiple Markov chains, multiple stochastic solutions of the model parameters can be obtained simultaneously, and the posterior probability density distribution of the model parameters can be simulated effectively. The posterior mean is treated as the optimal solution of the model to be inverted.Besides, the variance and confidence interval are utilized to evaluate the uncertainties of the estimated results, so as to realize the simultaneous estimation of reservoir elasticity, physical properties, discrete lithofacies and dry rock skeleton. The validity of the proposed approach is verified by theoretical tests and one real application case in eastern China.     

弹性波阻抗在时移地震中的应用分析

常规声波波阻抗由于缺乏横波的信息,使得它对于流体的变化很不敏感,而弹性波阻抗反演,除了与纵波速度,密度有关之外,还与横波速度、入射角度有关,由于考虑了AVO(Amplitude Variation with Offset)效应,使得对流体和岩性的预测能力增强.本文将弹性波阻抗反演应用于时移地震中,采用AVA(Amplitude Variation with Angle)约束稀疏脉冲反演来进行弹性波阻抗反演,得到纵横波阻抗等弹性参数,通过交会图分析,将弹性参数转换为油藏参数,如预测含水饱和度的变化,通过本方法可增强对储层流体的预测,提高储层管理能力.     

Bayesian inversion of time‐lapse seismic data for the estimation of static reservoir properties and dynamic property changes

Seismic conditioning of static reservoir model properties such as porosity and lithology has traditionally been faced as a solution of an inverse problem. Dynamic reservoir model properties have been constrained by time‐lapse seismic data. Here, we propose a methodology to jointly estimate rock properties (such as porosity) and dynamic property changes (such as pressure and saturation changes) from time‐lapse seismic data. The methodology is based on a full Bayesian approach to seismic inversion and can be divided into two steps. First we estimate the conditional probability of elastic properties and their relative changes; then we estimate the posterior probability of rock properties and dynamic property changes. We apply the proposed methodology to a synthetic reservoir study where we have created a synthetic seismic survey for a real dynamic reservoir model including pre‐production and production scenarios. The final result is a set of point‐wise probability distributions that allow us to predict the most probable reservoir models at each time step and to evaluate the associated uncertainty. Finally we also show an application to real field data from the Norwegian Sea, where we estimate changes in gas saturation and pressure from time‐lapse seismic amplitude differences. The inverted results show the hydrocarbon displacement at the times of two repeated seismic surveys.     

随机反演在储层预测中的应用

针对隐蔽油气藏储层预测的需要,开展了地震反演研究,根据目前的实际应用将储层预测中的基于模型的地震反演分为三个实施阶段:即构造反演、声波波阻抗或弹性波阻抗反演以及岩性反演,并对每个阶段的目的、关键技术及其原理进行了详细描述,尤其是详细描述了基于马尔科夫链的蒙特卡罗随机模拟技术.最后给出了一个综合应用测井、地质、地震资料进行反演,从而进行储层预测的实例.     

Integrating facies-based Bayesian inversion and supervised machine learning for petro-facies characterization in the Snadd Formation of the Goliat Field,south-western Barents Sea

Seismic petro-facies characterization in low net-to-gross reservoirs with poor reservoir properties such as the Snadd Formation in the Goliat field requires a multidisciplinary approach. This is especially important when the elastic properties of the desired petro-facies significantly overlap. Pore fluid corrected endmember sand and shale depth trends have been used to generate stochastic forward models for different lithology and fluid combinations in order to assess the degree of separation of different petro-facies. Subsequently, a spectral decomposition and blending of selected frequency volumes reveal some seismic fluvial geomorphological features. We then jointly inverted for impedance and facies within a Bayesian framework using facies-dependent rock physics depth trends as input. The results from the inversion are then integrated into a supervised machine learning neural network for effective porosity discrimination. Probability density functions derived from stochastic forward modelling of endmember depth trends show a decreasing seismic fluid discrimination with depth. Spectral decomposition and blending of selected frequencies reveal a dominant NNE trend compared to the regional SE–NW pro-gradational trend, and a local E–W trend potentially related to fault activity at branches of the Troms-Finnmark Fault Complex. The facies-based inversion captures the main reservoir facies within the limits of the seismic bandwidth. Meanwhile the effective porosity predictions from the multilayer feed forward neural network are consistent with the inverted facies model, and can be used to qualitatively highlight the cleanest regions within the inverted facies model. A combination of facies-based inversion and neural network improves the seismic reservoir delineation of the Snadd Formation in the Goliat Field.     

Look-ahead vertical seismic profiling inversion approach for density and compressional wave velocity in Bayesian framework

To reduce drilling uncertainties, zero-offset vertical seismic profiles can be inverted to quantify acoustic properties ahead of the bit. In this work, we propose an approach to invert vertical seismic profile corridor stacks in Bayesian framework for look-ahead prediction. The implemented approach helps to successfully predict density and compressional wave velocity using prior knowledge from drilled interval. Hence, this information can be used to monitor reservoir depth as well as quantifying high-pressure zones, which enables taking the correct decision during drilling. The inversion algorithm uses Gauss–Newton as an optimization tool, which requires the calculation of the sensitivity matrix of trace samples with respect to model parameters. Gauss–Newton has quadratic rate of convergence, which can speed up the inversion process. Moreover, geo-statistical analysis has been used to efficiently utilize prior information supplied to the inversion process. The algorithm has been tested on synthetic and field cases. For the field case, a zero-offset vertical seismic profile data taken from an offshore well were used as input to the inversion algorithm. Well logs acquired after drilling the prediction section was used to validate the inversion results. The results from the synthetic case applications were encouraging to accurately predict compressional wave velocity and density from just a constant prior model. The field case application shows the strength of our proposed approach in inverting vertical seismic profile data to obtain density and compressional wave velocity ahead of a bit with reasonable accuracy. Unlike the commonly used vertical seismic profile inversion approach for acoustic impedance using simple error to represent the prior covariance matrix, this work shows the importance of inverting for both density and compressional wave velocity using geo-statistical knowledge of density and compressional wave velocity from the drilled section to quantify the prior covariance matrix required during Bayesian inversion.     

非均质天然气藏的岩石物理模型及含气饱和度反演

非均质气藏中,天然气一般呈"斑块状"分布于含水岩石内部,这种非均匀分布特征会导致地震波显著的频散与衰减现象.为发展适用于碳酸盐岩储层中流体检测的岩石物理模型,本文基于Biot-Rayleigh波动方程,实现了对非饱和岩石的多尺度理论建模,预测了不同尺度下波响应与岩性、流体间的定量联系.将此项建模技术应用于阿姆河右岸的灰岩气藏,给出了多尺度的岩石物理学图板.通过与实验数据、测井精细解释结果及地震数据的对比分析,本文论证了图板的正确性与可适用性.结合叠后波阻抗反演与叠前弹性参数反演,基于地震资料进行了储层含气饱和度与孔隙度的反演,反演结果与各井实际的产气情况吻合.     

基于统计岩石物理模型的各向异性页岩储层参数反演

提出了各向异性页岩储层统计岩石物理反演方法.通过统计岩石物理模型建立储层物性参数与弹性参数的定量关系,使用测井数据及井中岩石物理反演结果作为先验信息,将地震阻抗数据定量解释为储层物性参数、各向异性参数的空间分布.反演过程在贝叶斯框架下求得储层参数的后验概率密度函数,并从中得到参数的最优估计值及其不确定性的定量描述.在此过程中综合考虑了岩石物理模型对复杂地下介质的描述偏差和地震数据中噪声对反演不确定性的影响.在求取最大后验概率过程中使用模拟退火优化粒子群算法以提高收敛速度和计算准确性.将统计岩石物理技术应用于龙马溪组页岩气储层,得到储层泥质含量、压实指数、孔隙度、裂缝密度等物性,以及各向异性参数的空间分布及相应的不确定性估计,为页岩气储层的定量描述提供依据.     

储层弹性与物性参数地震叠前同步反演的确定性优化方法

储层弹性与物性参数可直接应用于储层岩性预测和流体识别,是储层综合评价和油气藏精细描述的基本要素之一.现有的储层弹性与物性参数地震同步反演方法大都基于Gassmann方程,使用地震叠前数据,通过随机优化方法反演储层弹性与物性参数;或基于Wyllie方程,使用地震叠后数据,通过确定性优化方法反演储层弹性与物性参数.本文提出一种基于Gassmann方程、通过确定性优化方法开展储层弹性和物性参数地震叠前反演的方法,该方法利用Gassmann方程建立储层物性参数与叠前地震观测数据之间的联系,在贝叶斯反演框架下以储层弹性与物性参数的联合后验概率为目标函数,通过将目标函数的梯度用泰勒公式展开得到储层弹性与物性参数联合的方程组,其中储层弹性参数对物性参数的梯度用差分形式表示,最后通过共轭梯度算法迭代求解得到储层弹性与物性参数的最优解.理论试算与实际资料反演结果证明了方法的可行性.     

弹性阻抗反演及应用研究

利用常规叠后波阻抗反演方法不能得到可靠的波阻抗和其它岩性信息.本文运用弹性阻抗反演理论对某地区的地震资料进行了分析研究,应用实例表明,用弹性阻抗反演方法可得到较理想的反演结果,且从反演所得到的波阻抗剖面中能提取出许多岩性参数,这些岩性参数能反映岩性和流体特征.与传统的波阻抗反演相比,弹性阻抗反演具有信息量丰富、分辨率高的特点;与定性的AVO反演相比,该方法达到了定量的程度,而且速度快.     

基于自适应杂交遗传算法的CO_2地质封存的储层参数反演研究

二氧化碳地质封存是减少温室气体排放和减缓温室效应的重要手段.二氧化碳封存的一个重要组成部分是地震监测,即用地震的方法监测封存后的二氧化碳的分布变化.为了实现这个目标,需要建立储层参数与地震性质之间的关系(岩石物理模型)和从地震监测数据中反演获得储层流体的饱和度等参数.首先,本文以Biot理论为基础,结合多相流模型研究了多个物理参数(孔隙度、二氧化碳饱和度、温度和压力等)对同时含有二氧化碳和水的孔隙介质的波速和衰减等属性的影响.结果表明:孔隙度和二氧化碳饱和度对岩石的频散和衰减属性影响强烈,而温度和压力通过孔隙流体性质对岩石的波速产生影响.然后,本文基于含多相流的Biot理论,应用抗干扰能力强、且具有更好的局部搜索能力和抗早熟能力的自适应杂交遗传算法对实际数据进行了反演研究.对岩心实验数据的反演研究表明了算法的有效性,而且表明含多相流的Biot理论能够很好地解释水和二氧化碳饱和岩石的波速特征.最后,我们将自适应杂交遗传算法应用于实际封存项目的地震监测数据,获得了封存后不同时期的二氧化碳饱和度,达到了用地震方法监测二氧化碳分布的目的.     

Prestack amplitude versus angle inversion for Young's modulus and Poisson's ratio based on the exact Zoeppritz equations

Elastic parameters such as Young's modulus, Poisson's ratio, and density are very important characteristic parameters that are required to properly characterise shale gas reservoir rock brittleness, evaluate gas characteristics of reservoirs, and directly interpret lithology and oil‐bearing properties. Therefore, it is significant to obtain accurate information of these elastic parameters. Conventionally, they are indirectly calculated by the rock physics method or estimated by approximate formula inversion. The cumulative errors caused by the indirect calculation and low calculation accuracy of the approximate Zoeppritz equations make accurate estimation of Young's modulus, Poisson's ratio, and density difficult in the conventional method. In this paper, based on the assumption of isotropy, we perform several substitutions to convert the Zoeppritz equations from the classical form to a new form containing the chosen elastic constants of Young's modulus, Poisson's ratio, and density. The inversion objective function is then constructed by utilising Bayesian theory. Meanwhile, the Cauchy distribution is introduced as a priori information. We then combine the idea of generalised linear inversion with an iterative reweighed least squares algorithm in order to solve the problem. Finally, we obtain the iterative updating formula of the three elastic parameters and achieve the direct inversion of these elastic parameters based on the exact Zoeppritz equations. Both synthetic and field data examples show that the new method is not only able to obtain the two elastic parameters of Young's modulus and Poisson's ratio stably and reasonably from prestack seismic data but also able to provide an accurate estimation of density information, which demonstrates the feasibility and effectiveness of the proposed method. The proposed method offers an efficient seismic method to identify a “sweet spot” within a shale gas reservoir.     

Seismic refraction traveltime inversion for static corrections in a glaciated shield rock environment: a case study

A traveltime inversion technique is applied to model the upper ∼40 m of the subsurface of a glaciated shield rock area in order to calculate static corrections for a multi-azimuth multi-depth walk-away vertical seismic profile and a surface seismic reflection profile. First break information from a seismic refraction survey is used in conjunction with a ray-tracing program and an iterative damped least-squares inversion algorithm to create a two-dimensional model of the subsurface. The layout of the seismic survey required crooked seismic lines and substantial gaps in the source and receiver coverage to be accounted for. Additionally, there is substantial topographical variation and a complex geology consisting of glaciofluvial sediment and glacial till overlying a crystalline bedrock. The resolution and reliability of the models is measured through a parameter perturbation technique, normalized χ² values, root means square traveltime residuals and comparison to known geology.