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AVA(Amplitude Versus Angle)技术是基于常规介质模型(均匀各向同性介质模型)发展起来的,由于忽略了储层的孔隙结构和充填流体的影响,造成AVA特征中是部分对地震波能量的吸收和衰减作用反映不足.从振幅特征方程的实际应用出发,建立起各参数与常规岩性参数之间的关系.以Gassmann方程与Biot理论为基础,推导出振幅特征方程中弹性参数与常规岩性、储层参数(如:纵波速度、横波速度、密度、孔隙度、流体参数Kf)之间的转化关系式,详细研究饱和流体多孔介质模型中的AVA特征,比较该介质模型与常规介质模型在AVA特征上的差异,并将饱和流体多孔介质AVA技术应用于川西凹陷深层须家河组储层预测,通过多波AVA储层参数的反演研究,为直接利用地震资料进行储层识别并进一步识别其流体特征提供了一种有力手段. 相似文献
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常规AVA三参数反演方法均基于横波速度与纵波速度之比γ为常数这一假设条件,且常被近似地取为0.5。然而在许多情况下γ并不为常数,而是在横向与纵向都渐变。若一概假定γ等于0.5,反演出的岩性参数势必要偏离真实值,因此有必要合理地选择γ。笔者基于贝叶斯理论,提出逐次迭代非线性AVA的反演方法。该方法把γ看成横向与纵向都渐变的反演初始背景,通过给定初始模型计算初始背景γ,并采用逐次迭代的策略求解该反演问题,解决了关于γ的选取问题以及由于引入变γ值而带来的非线性问题,提高了AVA三参数反演结果的精确度。 相似文献
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Mattia Aleardi 《Geophysical Prospecting》2020,68(9):2738-2761
Markov chain Monte Carlo algorithms are commonly employed for accurate uncertainty appraisals in non-linear inverse problems. The downside of these algorithms is the considerable number of samples needed to achieve reliable posterior estimations, especially in high-dimensional model spaces. To overcome this issue, the Hamiltonian Monte Carlo algorithm has recently been introduced to solve geophysical inversions. Different from classical Markov chain Monte Carlo algorithms, this approach exploits the derivative information of the target posterior probability density to guide the sampling of the model space. However, its main downside is the computational cost for the derivative computation (i.e. the computation of the Jacobian matrix around each sampled model). Possible strategies to mitigate this issue are the reduction of the dimensionality of the model space and/or the use of efficient methods to compute the gradient of the target density. Here we focus the attention to the estimation of elastic properties (P-, S-wave velocities and density) from pre-stack data through a non-linear amplitude versus angle inversion in which the Hamiltonian Monte Carlo algorithm is used to sample the posterior probability. To decrease the computational cost of the inversion procedure, we employ the discrete cosine transform to reparametrize the model space, and we train a convolutional neural network to predict the Jacobian matrix around each sampled model. The training data set for the network is also parametrized in the discrete cosine transform space, thus allowing for a reduction of the number of parameters to be optimized during the learning phase. Once trained the network can be used to compute the Jacobian matrix associated with each sampled model in real time. The outcomes of the proposed approach are compared and validated with the predictions of Hamiltonian Monte Carlo inversions in which a quite computationally expensive, but accurate finite-difference scheme is used to compute the Jacobian matrix and with those obtained by replacing the Jacobian with a matrix operator derived from a linear approximation of the Zoeppritz equations. Synthetic and field inversion experiments demonstrate that the proposed approach dramatically reduces the cost of the Hamiltonian Monte Carlo inversion while preserving an accurate and efficient sampling of the posterior probability. 相似文献
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转换波AVA反演是利用转换波资料获取地下弹性参数的有效手段,AVA道集的准确抽取是利用AVA道集进行转换波资料反演的前提。基于模型抽取共转换点道集的处理思路是：首先依据目的层构造模型,采用基于模型的转换波速度分析方法获取目标层的纵横波速度比,在此基础上计算各道对应的转换点位置,进而抽取共转换点道集;以获得的共转换点道集为输入数据,采用三参量速度分析与动校正技术实现转换波资料动校正;在均方根意义下通过求取转换波的射线路径获得入射角信息并最终抽取转换波AVA反演道集。另外为消除因入射角分布不均造成的空道现象,提出了大面元法,该方法不仅对缺失的入射角有补偿作用,还能在一定程度上提高地震资料的信噪比。 相似文献
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共偏移距道集已被广泛地应用于地震速度建模及振幅随偏移距变化(AVO)的研究中,但复杂构造及射线多路径产生的共偏移距道集不保幅性等一系列缺陷给AVO研究带来很大的困难.共反射角道集包含有能反映地下速度和岩性变化的信息,更有利于速度模型优化、地震振幅属性分析及地下岩性和断裂的研究.本文通过研究共反射角深度偏移方法和理论,完善了基于目标的共反射角深度偏移技术,提出了获得相对保幅共反射角道集方法.该方法克服了共偏移距域道集在复杂介质中遇到的困难,更能有效地反映波场和地质结构方面的信息.通过理论模型数据进行了试算,并采用实际地震数据对此方法进行了验证,在陡倾角成像方面取得较好效果. 相似文献
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Leonardo Azevedo Rúben Nunes Amílcar Soares Guenther Schwedersky Neto Teresa S. Martins 《Geophysical Prospecting》2018,66(Z1):116-131
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. 相似文献
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In geophysical inverse problems, the posterior model can be analytically assessed only in case of linear forward operators, Gaussian, Gaussian mixture, or generalized Gaussian prior models, continuous model properties, and Gaussian-distributed noise contaminating the observed data. For this reason, one of the major challenges of seismic inversion is to derive reliable uncertainty appraisals in cases of complex prior models, non-linear forward operators and mixed discrete-continuous model parameters. We present two amplitude versus angle inversion strategies for the joint estimation of elastic properties and litho-fluid facies from pre-stack seismic data in case of non-parametric mixture prior distributions and non-linear forward modellings. The first strategy is a two-dimensional target-oriented inversion that inverts the amplitude versus angle responses of the target reflections by adopting the single-interface full Zoeppritz equations. The second is an interval-oriented approach that inverts the pre-stack seismic responses along a given time interval using a one-dimensional convolutional forward modelling still based on the Zoeppritz equations. In both approaches, the model vector includes the facies sequence and the elastic properties of P-wave velocity, S-wave velocity and density. The distribution of the elastic properties at each common-mid-point location (for the target-oriented approach) or at each time-sample position (for the time-interval approach) is assumed to be multimodal with as many modes as the number of litho-fluid facies considered. In this context, an analytical expression of the posterior model is no more available. For this reason, we adopt a Markov chain Monte Carlo algorithm to numerically evaluate the posterior uncertainties. With the aim of speeding up the convergence of the probabilistic sampling, we adopt a specific recipe that includes multiple chains, a parallel tempering strategy, a delayed rejection updating scheme and hybridizes the standard Metropolis–Hasting algorithm with the more advanced differential evolution Markov chain method. For the lack of available field seismic data, we validate the two implemented algorithms by inverting synthetic seismic data derived on the basis of realistic subsurface models and actual well log data. The two approaches are also benchmarked against two analytical inversion approaches that assume Gaussian-mixture-distributed elastic parameters. The final predictions and the convergence analysis of the two implemented methods proved that our approaches retrieve reliable estimations and accurate uncertainties quantifications with a reasonable computational effort. 相似文献
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Investigating a time‐shift extended imaging condition in a Kirchhoff pre‐stack depth migration algorithm 
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下载免费PDF全文 Gareth Shane O'Brien Sean Joseph Delaney Michael Igoe John Doherty Andrew Colhoun 《Geophysical Prospecting》2018,66(4):688-706
Extracting true amplitude versus angle common image gathers is one of the key objectives in seismic processing and imaging. This is achievable to different degrees using different migration techniques (e.g., Kirchhoff, wavefield extrapolation, and reverse time migration techniques) and is a common tool in exploration, but the costs can vary depending on the selected migration algorithm and the desired accuracy. Here, we investigate the possibility of combining the local‐shift imaging condition, specifically the time‐shift extended imaging condition, for angle gathers with a Kirchhoff migration. The aims are not to replace the more accurate full‐wavefield migration but to offer a cheaper alternative where ray‐based methods are applicable and to use Kirchhoff time‐lag common image gathers to help bridge the gap between the traditional offset common image gathers and reverse time migration angle gathers; finally, given the higher level of summation inside the extended imaging migration, we wish to understand the impact on the amplitude versus angle response. The implementation of the time‐shift imaging condition along with the computational cost is discussed, and results of four different datasets are presented. The four example datasets, two synthetic, one land acquisition, and a marine dataset, have been migrated using a Kirchhoff offset method, a Kirchhoff time‐shift method, and, for comparison, a reverse time migration algorithm. The results show that the time‐shift imaging condition at zero time lag is equivalent to the full offset stack as expected. The output gathers are cleaner and more consistent in the time‐lag‐derived angle gathers, but the conversion from time lag to angle can be considered a post‐processing step. The main difference arises in the amplitude versus offset/angle distribution where the responses are different and dramatically so for the land data. The results from the synthetics and real data show that a Kirchhoff migration with an extended imaging condition is capable of generating subsurface angle gathers. The same disadvantages with a ray‐based approach will apply using the extended imaging condition relative to a wave equation angle gather solution. Nevertheless, using this approach allows one to explore the relationship between the velocity model and focusing of the reflected energy, to use the Radon transformation to remove noise and multiples, and to generate consistent products from a ray‐based migration and a full‐wave equation migration, which can then be interchanged depending on the process under study. 相似文献
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本文将逐点匹配滤波思想应用于叠前AVA反演,提出了一种反射界面自动拾取方法,从而将叠前非线性反演问题转化为两步线性反演进行求解.首先基于逐点计算的方式获取每个采样点对应的残差值,然后在残差曲线上自动拾取波谷以定位反射界面,最后在频率域采用最小二乘反演对叠前参数进行更准确的估计.相比于传统的同时估计反射界面位置和幅值的非线性反演方法,该方法不需要关于参数的个数、分布等先验假设,并且具有运算速度快、求解稳定的优点.模型试验以及实际地震资料处理验证了该方法的有效性. 相似文献