AVO approximation for PS-wave and its application in PP/PS joint inversion

Multi-component exploration has many advantages over ordinary P-wave exploration. PP/PS joint AVO analysis and inversion are useful and powerful methods to discriminate between reservoir and non-productive lithology. In this paper, we derive a new PS-wave reflection coefficient approximation equation which is more accurate at larger incidence angles. The equation is simplified for small incidence angles, which makes AVO analysis clearer and easier for angles less than 30 degrees. Based on this approximation, a PP/PS joint inversion is introduced. A real data example shows that oil sands, brine sands and shales can be differentiated based on the P- to S-wave velocity ratio from the PP/PS joint inversion. Fluid factors and Poisson＇s ratio also indicate an anomaly in the target zone at the oil well location.

Joint PP and PS AVO inversion based on Bayes theorem

Based on the Aki-Richards approximate equations for reflection coefficients and Bayes theorem, we developed an inversion method to estimate P- and S-wave velocity contrasts and density contrast from combined PP and PS data. This method assumes that the parameters satisfy a normal distribution and introduces the covariance matrix to describe the degree of correlation between the parameters and thus to improve the inversion stability. Then, we suppose that the parameter sequence is subject to the Cauchy distribution and employs another matrix Q to describe the parameter sequence sparseness to improve the inversion result resolution. Tests on both synthetic and real multi-component data prove that this method is valid, efficient, more stable, and more accurate compared to methods using PP data only.

Elastic modulus extraction based on generalized pre-stack PP–PS wave joint linear inversion

Joint PP–PS inversion offers better accuracy and resolution than conventional P-wave inversion. P-and S-wave elastic moduli determined through data inversions are key parameters for reservoir evaluation and fluid characterization. In this paper, starting with the exact Zoeppritz equation that relates P-and S-wave moduli, a coefficient that describes the reflections of P-and converted waves is established. This method effectively avoids error introduced by approximations or indirect calculations, thus improving the accuracy of the inversion results. Considering that the inversion problem is ill-posed and that the forward operator is nonlinear, prior constraints on the model parameters and modified low-frequency constraints are also introduced to the objective function to make the problem more tractable. This modified objective function is solved over many iterations to continuously optimize the background values of the velocity ratio, which increases the stability of the inversion process. Tests of various models show that the method effectively improves the accuracy and stability of extracting P and S-wave moduli from underdetermined data. This method can be applied to provide inferences for reservoir exploration and fluid extraction.

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.

Bayesian Markov chain Monte Carlo inversion for anisotropy of PP-and PS-wave in weakly anisotropic and heterogeneous media

A single set of vertically aligned cracks embedded in a purely isotropic background may be con- sidered as a long-wavelength effective transversely iso- tropy （HTI） medium with a horizontal symmetry axis. The crack-induced HTI anisotropy can be characterized by the weakly anisotropic parameters introduced by Thomsen. The seismic scattering theory can be utilized for the inversion for the anisotropic parameters in weakly aniso- tropic and heterogeneous HTI media. Based on the seismic scattering theory, we first derived the linearized PP- and PS-wave reflection coefficients in terms of P- and S-wave impedances, density as well as three anisotropic parameters in HTI media. Then, we proposed a novel Bayesian Mar- kov chain Monte Carlo inversion method of PP- and PS- wave for six elastic and anisotropic parameters directly. Tests on synthetic azimuthal seismic data contaminated by random errors demonstrated that this method appears more accurate, anti-noise and stable owing to the usage of the constrained PS-wave compared with the standards inver- sion scheme taking only the PP-wave into account.

PP-PS协同反演技术预测大庆深层火山岩含气储层

徐深气田深层火山岩含气储层含气检测尚缺乏有效的方法,传统的纵波反演由于反演过程中仅仅使用单个模型的波,反演结果存在多解性.PP、PS联合反演方法较好地解决了多解性难题,反演结果比较稳定,可信度较高.通过在大庆深层火山岩探区采集2D3C多波资料,并进行处理技术攻关,综合利用测井、VSP等资料,利用PP、PS联合反演技术进行了纵、横波速度比弹性参数反演.结果表明,储层特征与联合反演的Gamma场剖面特征对应较好,测井资料显示的储层特征在反演剖面上也有较好的显示,从而证明了PP、PS协同反演技术在深层火山岩含气储层预测上的有效性.

基于时间—振幅双γ模型的PP-PS波联合反演

在常规PP-PS波反演中利用走时计算纵横波速度比(γ)模型以对PP波、PS波进行时间匹配,这在构造复杂或岩性横向变化剧烈区会引起严重误差;另外常规PP-PS波反演通常假设γ=2来进行,这往往导致反演结果不准确。因此,本文介绍一种利用时间走时和振幅能量计算的双γ模型方法来求取最佳γ,使得PP波和PS波得到最佳匹配;在此基础上利用纵波截距、梯度和横波梯度三个独立参数反演得到纵波速度、横波速度和密度及泊松比等岩性参数。反演过程使用模拟退火方法进行全局寻优,可以有效避免局部极值。将该方法应用到苏里格气田某区块,预测结果的总符合率达到80%以上。

平均入射角道集PP波与PS波联合反演

在界面两侧地层的弹性参数弱反差的假设难以成立的情况下,本文提出用平均入射角道集进行PP波与PS波的联合反演.首先,在PP波与PS波AVA(amplitude versus angle,振幅随入射角变化)道集的基础上,分别选择小入射角范围与大入射角范围的AVA道集进行局部加权叠加,以获得由两个角度组成的平均入射角道集,并作为后续反演的输入数据.然后,再通过最小二乘原理建立了PP波与PS波联合反演目标函数,推导了模型修改量的向量公式,建立了平均入射角道集联合反演的流程.模型数据与实际数据的测试结果表明:在信噪比较低、地层弹性参数反差较大、层厚较薄的情况下,该反演方法的精度在很大程度上超过了基于近似反射系数的反演方法,为复杂油气藏勘探提供了新的思路.

联合PP、PS波叠前AVA反演方法

从单一PP波数据中提取出来的弹性参数与岩性以及孔隙内流体之间的联系是不充分的,这就造成了在油藏的解释中包含有大量的不确定性。通过加入PS波数据信息,可以有效降低反演的多解性,提高反演的稳定性。笔者所研究的反演方法是基于精确的Zoeppritz方程,利用广义线性反演方法,通过迭代的思想来反演出纵波速度、横波速度和密度。该方法相对单一PP反演可以有效提高横波速度的估计精度。

PP-PS联合反演含气性预测方法及应用

讨论了利用PP波和PS转换横波进行叠前联合反演的理论和实现方法,结合某油田实际多波地震资料,在叠前联合反演的基础上,对深层火成岩气藏进行了含气性预测。经钻井资料验证,根据联合反演资料预测的气藏结果与井资料吻合较好。实践证明,纵横波叠前联合反演可以避免单纯使用纵波信息进行弹性反演而造成的岩石弹性参数的多解性,提高气藏预测的准确性。

叠前PS波Q值提取与稳定高效的反Q滤波算法

多波多分量地震勘探是进行岩性油气藏和隐蔽油气藏勘探的一种非常有潜力的方法。但由于实际地层是黏弹介质,造成地震波能量衰减和波形畸变,导致地震资料信噪比和分辨率难以满足油气勘探的精度要求。为更好地利用纵横波信息,需要对PP波和PS波进行振幅和相位补偿。通过解析法推导出一种从叠前转换波共炮点道集中提取横波Q值的方法,将叠后稳定高效的反Q滤波算法推广到叠前纵波和转换波的反Q滤波中,改进了沿着纵波和转换波的传播路径进行补偿的叠前反Q滤波算法,并进一步将常Q层内的纵波和转换波反Q滤波振幅补偿算子解耦为时间项和频率项的乘积,在保证补偿效果稳定的前提下,计算效率得到显著提高。

多分量地震在致密气储层精细描述中的应用

四川盆地川中秋林地区沙溪庙组河道砂岩具有期次多、砂体叠置的地质特征,精细刻画砂体及其叠置关系对水平井的部署至关重要。由于常规地震勘探方法难以取得理想效果,因此在研究区开展了多分量地震采集探索。研究表明:①沙溪庙组砂岩与泥岩的纵波速度基本相同,而横波速度差别大,因此利用转换波区分岩性具有较大优势。②纵波数据对砂体变化不敏感,地震波形难以反映砂体的叠置关系。转换波虽然主频低于纵波,但对岩性变化敏感,地震波形与砂体变化有很好的对应关系。③纵波叠前反演的横波速度精度较低,纵、横波联合反演的横波速度具有很高的预测精度,能够准确刻画砂体及其叠置关系。研究结果证明多分量地震在精细描述砂岩储层方面具有潜力,为沙溪庙组致密砂岩气藏精准、高效开发提供了数据基础。

基于贝叶斯理论的叠前多波联合反演弹性模量方法

AVO反演可以获得地层岩性和流体信息,而叠前反演问题都是高维的和非适定的,因此获得可靠稳定的解对叠前反演至关重要.本文给出了一种基于贝叶斯理论的纵波和转换波联合反演密度比和模量比的方法.鉴于剪切模量比、体积模量比可以更好地指示油气,基于岩石物理中速度比与模量比之间的关系,将此关系式代入Zoeppritz方程的近似形式Aki-Richards公式中,得到与模量比有关的反射系数近似公式.联合纵波和转换波,利用最小二乘准则构建目标函数,最终反演出密度比、剪切模量比、体积模量比三个参数.在反演过程中引入贝叶斯理论,假定先验信息服从高斯分布,待求参数服从改进的Cauchy分布,并去除待求参数之间的相关性.利用模型数据和实际数据对本文方法进行测试,并与常规的单独利用纵波数据来反演方法进行比较,结果表明联合反演稳定性更好、精度更高、抗噪音能力更强,验证了本文方法的可行性和有效性.

纵波和转换波联合反演技术在松辽盆地北部LMD地区的应用

常规纵波反演技术基于纵波地震资料,反演结果具有多解性。纵波和转换波联合反演技术基于纵波和转换波地震资料,由于增加了转换波地震资料这一约束条件,大大降低了反演结果的多解性,从而提高了储层预测的精度。纵波和转换波联合反演方法的基本原理是,通过建立纵波和转换波的反射系数线性方程,基于三维三分量地震资料,计算地层的纵波阻抗、横波阻抗和密度的反射率。首先基于正演模型,讨论了反演方法的精度;然后基于松辽盆地北部LMD地区的三维三分量地震资料,针对某气顶型油藏进行了反演,反演结果与实际地质特征吻合。

纵、横波联合反演方法在海拉尔盆地贝39井区的应用

海拉尔贝39井区构造复杂,砂体边界识别难,常规纵波地震解释存在多解性,引入转换波信息可增加地震解释的可靠性.由测井曲线的岩石物理分析可知,在纵波阻抗-速度比双弹性空间可识别砂泥岩.本文根据海拉尔地区测井资料的特点,利用拟合方法重构横波测井曲线;基于海拉尔贝39井区三维三分量地震数据,采用放大比例尺层拉平的精细层位解释及纵波降频和转换波提频相结合的方法进行精细层位匹配,在此基础上,由纵、横波联合反演求出速度比属性,定性描述了储层的平面分布,与贝39井钻遇油层特征相符,解决了该地区的油水分布问题.应用属性切片技术得到油层组的砂体平面展布,砂体的展布特征与区域地质认识的物源沉积方向具有较好的吻合程度,并对贝39井区的油藏类型进行了重新认识.

转换波叠后联合反演技术在川西须家河组气藏的应用

川西坳陷上三叠统须家河组气藏为超致密超高压非常规近源气藏,具有地质条件复杂、成藏机理不清、地震资料有效频带窄与主频低等不利因素,勘探开发难度极大。常规纵波反演技术不能够充分地利用横波信息,无法提供与储层预测、含气性识别等相关的精确参数,难以有效地解决致密气藏的勘探难题。而转换波叠后联合反演技术同时利用全波测井、地震、地质等综合信息,反演出泊松比、纵横波速度比、纵横波阻抗、密度等与岩性和物性相关的重要参数,为致密油气藏的描述提供了更准确、有效的依据。进行转换波叠后联合反演之前,还需要对PP波与PS波进行匹配处理,消除反射波组的频率、相位、波形等差异,以建立准确的反演初始模型。将该技术应用到川西新场地区须家河组气藏描述,获得了良好的储层预测、岩性识别及含气性检测效果。

广义线性纵横波联合反演的GPU加速

文中进行了广义线性反演(GLI)的GPU加速研究,通过理论分析和模型数据检验表明,通过应用L1模稀疏脉冲反褶积,数据质量得到了显著提高。该方法不仅对多波数据适用,对单一纵、横波数据都可以进行反演。反演结果表明:1应用L1模稀疏脉冲反褶积可以在反演前消除子波的效应,从而使GLI在反射系数层面上发挥更好的作用。2将GPU并行计算技术整合到反演过程中,能够极大地提高计算速度和效率,对单个角度域共成像点道集(ADCIG)的加速达数百倍以上;在处理海量的叠前反演数据时,这种优势将更加明显。3在反演中采用并行计算,必须摒弃细粒度的任务设计思路,采用较粗粒度的设计,才能尽量减少延迟,提高计算效率。

转换波地震资料叠后反Q滤波及应用

转换波叠后剖面与纵波叠后剖面之间在分辨能力、剖面特征上还存在着较大的差异,为了使转换波剖面能尽量地与纵波剖面相匹配,采用反Q滤波方法,适当提高叠后转换波剖面的分辨能力,调整其相位、振幅、频率特征,以满足转换波和纵波联合解释和反演的需求。实际效果表明,该方法为后续纵波转换波联合解释及反演工作提供了条件。

地震波多分量联合反演在塔北隆起S油田的应用

三叠系是塔里木盆地S油田主要含油气层,三角洲前缘砂岩储集层物性好,但含气后纵波速度趋近于泥岩。以地震多波多分量资料为基础,用测井资料分析确定了有效储集层敏感参数;在纵波、转换波精细标定及匹配的基础上,开展纵波、转换波联合反演,得到了纵波阻抗、横波阻抗、纵横波速度比等参数。用横波阻抗有效识别出砂岩分布特征,用纵横波速度比预测了储集层的含油气性。结果表明,多波资料储集层预测较单一纵波预测精度更高。