基于改进反射系数近似方程的纵横波阻抗同步反演

基于地震波反射系数近似公式的叠前反演是油气勘探的重要工具.本文在已有研究的基础上,推导了一个改进的射线参数域地震纵波反射系数近似方程.该方程建立了地震纵波反射系数与纵波阻抗和横波阻抗的非线性关系,在中、小角度的范围内较现有的反射系数线性近似公式精度更高.另外,由于该方程仅包含纵波和横波阻抗反射系数项,因此基于新方程的反演能够有效地降低同步反演纵波速度、横波速度、密度三个参数的不适定性.在此基础上,结合广义线性反演法(GLI)理论和贝叶斯理论,相应地发展了一种叠前地震同步反演方法.模型测试和实际资料的应用表明,基于新方程的反演方法能够利用有限角度(偏移距)的数据稳定地反演纵波和横波阻抗,由于在反演过程中,不需要假设纵横波速度为常数,因此该方法还能有效地提高反演结果的精度.     

基于约束条件横波速度反演和流体替代

依据辽东湾地区一组在地层条件和不同流体相态下（气饱和与水饱和）实验室测定的岩石纵波速度、横波速度及密度等数据,讨论Gassmann方程在横波速度反演和流体替代方面的应用效果,指出岩石骨架剪切模量和体积模量估算精度是影响纵波、横波速度预测精度的关键因素之一,提出基于约束条件横波速度反演和流体替代方法,速度预测精度的改善十分明显.     

页岩气储层纵横波叠前联合反演方法

杨氏模量与密度乘积(Eρ)能够突显页岩气储层的异常特征,泊松比能够指示储层的含流体性.与常规叠前弹性参数反演相比,基于Eρ、泊松比和密度的叠前纵横波联合反演可以获得更加精确的弹性参数,为页岩气储层识别和流体预测提供可靠的依据.首先,推导了基于Eρ、泊松比和密度的纵波和转换波反射系数近似方程,利用典型模型对新推导的反射系数方程做精度分析,当入射角小于30°时,新推导的反射系数公式具有较高的精度;其次,充分利用纵波和转换波的信息,在贝叶斯的框架下,建立叠前纵横波联合反演流程,进行Eρ、泊松比和密度的直接反演,避免了间接反演带来的累积误差;最后,利用实际工区井模型数据进行算法测试,结果表明,该反演方法所获得的Eρ、泊松比和密度的估计值与真实值吻合较好,满足精细地震反演的精度要求.     

孔隙裂隙型砂岩横波速度预测方法

在地震资料的AVO分析以及储层预测的流体识别分析等中需要横波速度信息,然而目前多数地区缺少横波速度测井,既没有岩石物理测试也没有VSP提供的横波速度信息.以往AVO和储层反演中的横波速度通常依靠经验公式或从纵横波波速度比为常数的假设中得到.这些经验公式往往精度很低,对于AVO特性包括弹性阻抗等特性描述不准确.特别是对储层中流体变化引起的AVO异常无法准确描述.为了获得准确的横波速度信息,我们采用Gassmann方程为基础的孔隙介质理论以及Lee提出来的方法建立了孔隙介质的横波速度模型.以此模型为基础,利用Thomsen的裂隙介质理论建立更加符合实际的含有裂隙的孔隙砂岩模型.预测的横波速度与实际的横波速度的对比证明了本文方法的有效性.     

转换波叠前横波速度反演

摘要 地震波速度对地震资料偏移成像、岩性和岩相解释以及储层预测,均起着非常重要的作用. 在储层预测中,需要获得横波层速度信息,以减少仅用纵波速度在区分岩性和孔隙中流体类型以及含气饱和度方面的不确定性. 基于Zoeppritz方程组,提出了数值求解和速度扫描方法,对叠前转换波资料求取横波层速度. 理论模型和实际转换波资料的反演结果, 均表明该方法的效果较好.      

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.     

基于叠前弹性反演的含气储层预测技术

叠前弹性反演是目前岩性气藏储层预测的主要技术,仅仅使用纵波阻抗反演预测储层在理论和准确性方面都存在着一些不足,反演的多解性更是其致命的缺陷.本文从横波测井资料不同岩性、流体的岩石物理参数分析入手,优选能够区分岩性和流体的最佳敏感弹性参数,通过叠前反演获得反映储层岩性和含气性的弹性参数体,最后进行地震弹性参数交汇解释预测储层和识别流体.将纵向分辨率较高的测井岩石物理参数分析和横向分辨率较高的地震叠前反演结合起来,应用多个弹性参数,明显提高了储层预测的精度.地震数据中的AVO信息得到了充分的应用,采用纵波独立变量求解Zoeppritz方程.为有效储层预测和流体识别探索出了新途径.在靶区利用叠前弹性纵横波速比的属性,刻画出五级层序单砂体,在此基础上确定建议的水平井井位及其轨迹,通过叠前弹性反演比P波数据独立反演钻井成功率提高了10%以上.     

基于变形P-L模型的矩阵方程迭代精细横波预测

横波速度预测问题的关键有两个,一是如何建立合理的岩石物理模型,二是针对建立的横波预测目标函数,如何准确高效地求解.针对第一个问题,对Pride模型和Lee模型(P-L模型)进行变形,提出拟固结指数的概念,将干岩石模量和岩石基质模量相联系,变形后的P-L模型在没有降低P-L模型准确度的情况下简化了问题的复杂度,建立起了饱和流体岩石弹性模量与干岩石模量、岩石基质模量、混合流体模量之间的关系,进而计算理论上的纵波速度,并通过比较实测纵波速度与计算的理论纵波速度大小,最终建立了横波预测的目标函数.针对第二个问题,借鉴了地震反演的思路,将该目标函数的最优化问题转化为线性矩阵方程组迭代求解问题,通过几步迭代就可以求解出合适的拟固结指数,进而得到预测横波速度.实际验证和应用表明,该横波预测方法具有很好的稳定性和准确性,并且岩石物理模型的构建和目标函数的求解思路可用于其他储集类型地层的横波预测.     

带粒子滤波约束的PP-PS联合反演的稀疏解算法

随着地震勘探目标从构造型油气藏向岩性油气藏的转变,地震勘探难度日益增大,这就要求从地震数据中获得更多可靠且具有明确地质含义的属性信息,并充分利用这些属性信息来对储层的岩性、岩相进行分析.AVO三参数反演能够从振幅随炮检距的变化信息中直接提取纵波速度、横波速度以及密度来估计岩石和流体的性质,进而对储层进行预测.然而,AVO反演本身是一个不适定的问题,加上地震纵波反射系数对横波速度和密度的不敏感,会造成单纯利用纵波地震数据进行反演的结果误差大.随着地震接收和数据处理技术的发展,越来越多的学者对PP-PS联合反演方法进行了研究并在实际资料中得以运用.融合转换横波地震数据的联合反演在一定程度上提高了反演的精度,降低了解的不稳定性.但是在信噪比较低的情况下,联合反演的效果受到了限制.本文从优化理论出发,提出了基于粒子滤波提供先验知识的l₁范数约束极小化问题的稀疏解算法.并将上述方法运用到了不同的模型中,通过比较分析,证实了该方法在不同信噪比资料中的有效性和在信噪比较低情况下的优势.     

基于叠前地震纵横波模量直接反演的流体检测方法

流体因子是储层流体识别的重要方法,而叠前地震反演是获得流体因子的有效途径之一.本文从流体因子的构建出发,基于多孔弹性介质岩石物理模型,建立了流体因子与纵横波模量之间的直接关系,避免了流体因子计算所需的密度参数无法准确求取的问题,通过推导基于纵横波模量的Zeoppritz近似方程及弹性阻抗方程,探讨了基于弹性阻抗的纵横波模量直接反演方法,模型与实际应用表明,基于弹性阻抗的纵横波模量直接反演方法合理、可靠,减少了常规方法间接计算纵横波模量带来的累积误差,基于纵横波模量的流体因子计算方法有较好的实际应用效果.     

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.     

Geostatistical seismic Amplitude‐versus‐angle inversion

Seismic inversion plays an important role in reservoir modelling and characterisation due to its potential for assessing the spatial distribution of the sub‐surface petro‐elastic properties. Seismic amplitude‐versus‐angle inversion methodologies allow to retrieve P‐wave and S‐wave velocities and density individually allowing a better characterisation of existing litho‐fluid facies. We present an iterative geostatistical seismic amplitude‐versus‐angle inversion algorithm that inverts pre‐stack seismic data, sorted by angle gather, directly for: density; P‐wave; and S‐wave velocity models. The proposed iterative geostatistical inverse procedure is based on the use of stochastic sequential simulation and co‐simulation algorithms as the perturbation technique of the model parametre space; and the use of a genetic algorithm as a global optimiser to make the simulated elastic models converge from iteration to iteration. All the elastic models simulated during the iterative procedure honour the marginal prior distributions of P‐wave velocity, S‐wave velocity and density estimated from the available well‐log data, and the corresponding joint distributions between density versus P‐wave velocity and P‐wave versus S‐wave velocity. We successfully tested and implemented the proposed inversion procedure on a pre‐stack synthetic dataset, built from a real reservoir, and on a real pre‐stack seismic dataset acquired over a deep‐water gas reservoir. In both cases the results show a good convergence between real and synthetic seismic and reliable high‐resolution elastic sub‐surface Earth models.     

Coherence methods in mapping AVO anomalies and predicting P-wave and S-wave impedances

Filters for migrated offset substacks are designed by partial coherence analysis to predict ‘normal’ amplitude variation with offset (AVO) in an anomaly free area. The same prediction filters generate localized prediction errors when applied in an AVO‐anomalous interval. These prediction errors are quantitatively related to the AVO gradient anomalies in a background that is related to the minimum AVO anomaly detectable from the data. The prediction‐error section is thus used to define a reliability threshold for the identification of AVO anomalies. Coherence analysis also enables quality control of AVO analysis and inversion. For example, predictions that are non‐localized and/or do not show structural conformity may indicate spatial variations in amplitude–offset scaling, seismic wavelet or signal‐to‐noise (S/N) ratio content. Scaling and waveform variations can be identified from inspection of the prediction filters and their frequency responses. S/N ratios can be estimated via multiple coherence analysis. AVO inversion of seismic data is unstable if not constrained. However, the use of a constraint on the estimated parameters has the undesirable effect of introducing biases into the inverted results: an additional bias‐correction step is then needed to retrieve unbiased results. An alternative form of AVO inversion that avoids additional corrections is proposed. This inversion is also fast as it inverts only AVO anomalies. A spectral coherence matching technique is employed to transform a zero‐offset extrapolation or near‐offset substack into P‐wave impedance. The same technique is applied to the prediction‐error section obtained by means of partial coherence, in order to estimate S‐wave velocity to P‐wave velocity (V_S/V_P) ratios. Both techniques assume that accurate well ties, reliable density measurements and P‐wave and S‐wave velocity logs are available, and that impedance contrasts are not too strong. A full Zoeppritz inversion is required when impedance contrasts that are too high are encountered. An added assumption is made for the inversion to the V_S/V_P ratio, i.e. the Gassmann fluid‐substitution theory is valid within the reservoir area. One synthetic example and one real North Sea in‐line survey illustrate the application of the two coherence methods.     

富含煤层气储层的叠前地震反演预测方法研究——以山西晋东南测区为例

叠前地震反演技术能够将地震中振幅、偏移距和入射角等多种有效信息与油气的敏感参数结合起来,在有效识别油气藏"甜点"方面虽发挥了重要的作用,但对于煤层气的有效预测仍需研究.针对煤层气与石油天然气的赋存地质条件不同,本文以山西沁水盆地煤层气地震资料为例,借鉴叠前地震反演技术的多参数预测思想,通过模型正演和实测资料处理,得到纵波、横波速度和密度等不同的数据体,利用数据体和弹性参数关系式,采用AkiRichard近似公式与叠前宽角度反演联合的方法,寻找出富含煤层气储层的敏感参数.从而实现富含煤层气储层的有效预测.经与测井曲线结果对比,吻合率较高,说明了该方法具有可行性与实用性.     

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。     

Monte-Carlo Bayesian look-ahead inversion of walkaway vertical seismic profiles

We describe a method to invert a walkaway vertical seismic profile (VSP) and predict elastic properties (P‐wave velocity, S‐wave velocity and density) in a layered model looking ahead of the deepest receiver. Starting from Bayes's rule, we define a posterior distribution of layered models that combines prior information (on the overall variability of and correlations among the elastic properties observed in well logs) with information provided by the VSP data. This posterior distribution of layered models is sampled by a Monte‐Carlo method. The sampled layered models agree with prior information and fit the VSP data, and their overall variability defines the uncertainty in the predicted elastic properties. We apply this technique first to a zero‐offset VSP data set, and show that uncertainty in the long‐wavelength P‐wave velocity structure results in a sizable uncertainty in the predicted elastic properties. We then use walkaway VSP data, which contain information on the long‐wavelength P‐wave velocity (in the reflection moveout) and on S‐wave velocity and density contrasts (in the change of reflectivity with offset). The uncertainty of the look‐ahead prediction is considerably decreased compared with the zero‐offset VSP, and the predicted elastic properties are in good agreement with well‐log measurements.     

联合利用走时与波形反演技术研究地壳三维速度结构(Ⅱ)——应用

使用阻尼最小二乘法进行震源参数和地壳三维速度结构的走时联合反演．所用资料为Ｓ波和Ｐ波到时差,并用人工地震资料的二维解释结果作为三维速度模型的特定约束条件．为建立初始模型,又利用天然地震构成了准二维剖面．在走时反演基础上,利用遗传算法进行了几个地震事件的波形反演尝试,并对走时反演获得的地壳速度结构模型的局部进行了修正．以３４°～４２°Ｎ,９４°～１１２°Ｅ作为研究区域,在该区域中收集了１９８６年以来大量地震的Ｓ波和Ｐ波到时差资料,７条人工地震二维速度剖面资料和２个数字化地震台的几个地震的三分向记录资料．对这些资料进行了处理,最后得出了０～２５ｋｍ深度不同截面的速度分布,并对所得结果进行了分析．     

Elastic full waveform inversion of multi‐component OBC seismic data

Elastic full waveform inversion of seismic reflection data represents a data‐driven form of analysis leading to quantification of sub‐surface parameters in depth. In previous studies attention has been given to P‐wave data recorded in the marine environment, using either acoustic or elastic inversion schemes. In this paper we exploit both P‐waves and mode‐converted S‐waves in the marine environment in the inversion for both P‐ and S‐wave velocities by using wide‐angle, multi‐component, ocean‐bottom cable seismic data. An elastic waveform inversion scheme operating in the time domain was used, allowing accurate modelling of the full wavefield, including the elastic amplitude variation with offset response of reflected arrivals and mode‐converted events. A series of one‐ and two‐dimensional synthetic examples are presented, demonstrating the ability to invert for and thereby to quantify both P‐ and S‐wave velocities for different velocity models. In particular, for more realistic low velocity models, including a typically soft seabed, an effective strategy for inversion is proposed to exploit both P‐ and mode‐converted PS‐waves. Whilst P‐wave events are exploited for inversion for P‐wave velocity, examples show the contribution of both P‐ and PS‐waves to the successful recovery of S‐wave velocity.     

海洋拖缆地震资料初至波走时层析反演

初至波走时层析反演技术作为建立近地表速度模型的重要手段,是解决陆地资料复杂静校正问题的关键技术。而折射波广泛发育的海洋地震资料,对折射波信息的关注与运用并没有得到广泛的重视。本文首次将层析反演方法应用于海洋拖缆地震数据的近海底速度模型的建立。本文方法与陆地资料层析反演的主要区别在于:①在震源信号的最小相位化处理后进行初至时间的拾取,避免了混合相位子波初至拾取不准带来的误差;②以海水深度与海水速度作为反演约束条件,减小了迭代误差。实测二维资料的层析反演结果表明,本文方法可反演出较为精确的海洋地层速度结构。