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1.
岩相和储层物性参数是油藏表征的重要参数,地震反演是储层表征和油气藏勘探开发的重要手段.随机地震反演通常基于地质统计学理论,能够对不同类型的信息源进行综合,建立具有较高分辨率的储层模型,因而得到广泛关注.其中,概率扰动方法是一种高效的迭代随机反演策略,它能综合考虑多种约束信息,且只需要较少的迭代次数即可获得反演结果.在概率扰动的优化反演策略中,本文有效的联合多点地质统计学与序贯高斯模拟,并结合统计岩石物理理论实现随机反演.首先,通过多点地质统计学随机模拟,获得一系列等可能的岩相模型,扰动更新初始岩相模型后利用相控序贯高斯模拟建立多个储层物性参数模型;然后通过统计岩石物理理论,计算相应的弹性参数;最后,正演得到合成地震记录并与实际地震数据对比,通过概率扰动方法进行迭代,直到获得满足给定误差要求的反演结果.利用多点地质统计学,能够更好地表征储层空间特征.相控序贯高斯模拟的应用,能够有效反映不同岩相中储层物性参数的分布.提出的方法可在较少的迭代次数内同时获得具有较高分辨率的岩相和物性参数反演结果,模型测试和实际数据应用验证了方法的可行性和有效性. 相似文献
2.
叠前地质统计学反演将随机模拟与叠前反演相结合,不仅可以反演各种储层弹性参数,还提高了反演结果的分辨率.基于联合概率分布的直接序贯协模拟方法可以在原始数据域对数据进行模拟,不需要对数据进行高斯变换,拓展了地质统计学反演的应用范围;而联合概率分布的应用确保了反演参数之间相关性,提高了反演的精度.本文将基于联合概率分布的直接序贯协模拟方法与蒙特卡洛抽样算法相结合,参考全局随机反演策略,提出了基于蒙特卡洛优化算法的全局迭代地质统计学反演方法.为了提高反演的稳定性,我们修改了局部相关系数的计算公式,提出了一种新的基于目标函数的优化局部相关系数计算公式并应用到协模拟之中.模型测试及实际数据应用表明,该方法可以很好的应用于叠前反演之中. 相似文献
3.
A covariance-based model-fitting approach is often considered valid to represent field spatial variability of hydraulic properties. This study examines the representation of geologic heterogeneity in two types of geostatistical models under the same mean and spatial covariance structure, and subsequently its effect on the hydraulic response to a pumping test based on 3D high-resolution numerical simulation and field data. Two geostatistical simulation methods, sequential Gaussian simulation (SGS) and transition probability indicator simulation (TPROGS) were applied to create conditional realizations of alluvial fan aquifer systems in the Lawrence Livermore National Laboratory (LLNL) area. The simulated K fields were then used in a numerical groundwater flow model to simulate a pumping test performed at the LLNL site. Spatial connectivity measures of high-K materials (channel facies) captured connectivity characteristics of each geostatistical model and revealed that the TPROGS model created an aquifer (channel) network having greater lateral connectivity. SGS realizations neglected important geologic structures associated with channel and overbank (levee) facies, even though the covariance model used to create these realizations provided excellent fits to sample covariances computed from exhaustive samplings of TPROGS realizations. Observed drawdown response in monitoring wells during a pumping test and its numerical simulation shows that in an aquifer system with strongly connected network of high-K materials, the Gaussian approach could not reproduce a similar behavior in simulated drawdown response found in TPROGS case. Overall, the simulated drawdown responses demonstrate significant disagreement between TPROGS and SGS realizations. This study showed that important geologic characteristics may not be captured by a spatial covariance model, even if that model is exhaustively determined and closely fits the exponential function. 相似文献
4.
虽然基于地质统计学的随机反演方法能够有效融合测井资料中的高频信息,但计算效率低,占用内存大,限制了它在实际资料中的应用.本文在保留传统随机反演方法优点的基础上,创造性地引入傅里叶滑动平均(Fast Fourier Transform-Moving Average,FFT-MA)谱模拟进行频率域的地质统计模拟,并利用逐步变形算法(Gradual Deformation Method,GDM)确保模拟结果与实际地震数据的匹配,构建了基于FFT-MA谱模拟的新的快速随机反演方法.与常规随机反演相比,新方法不仅分辨率高,而且能够使反演解得到快速收敛,有效提高计算效率,减少内存占用.模型试算获得了与理论模型吻合度较好的高分辨率反演结果.实际资料分析也表明新方法所得到的高分辨率反演结果能够对薄互储层进行良好的展示,为薄储层的识别提供高效可靠的技术支持. 相似文献
5.
Jichun Wu Bill X. Hu 《Stochastic Environmental Research and Risk Assessment (SERRA)》2007,21(6):665-682
The unconditional stochastic studies on groundwater flow and solute transport in a nonstationary conductivity field show that
the standard deviations of the hydraulic head and solute flux are very large in comparison with their mean values (Zhang et al.
in Water Resour Res 36:2107–2120, 2000; Wu et al. in J Hydrol 275:208–228, 2003; Hu et al. in Adv Water Resour 26:513–531, 2003). In this study, we develop a numerical method of moments conditioning on measurements of hydraulic conductivity and head
to reduce the variances of the head and the solute flux. A Lagrangian perturbation method is applied to develop the framework
for solute transport in a nonstationary flow field. Since analytically derived moments equations are too complicated to solve
analytically, a numerical finite difference method is implemented to obtain the solutions. Instead of using an unconditional
conductivity field as an input to calculate groundwater velocity, we combine a geostatistical method and a method of moment
for flow to conditionally simulate the distributions of head and velocity based on the measurements of hydraulic conductivity
and head at some points. The developed theory is applied in several case studies to investigate the influences of the measurements
of hydraulic conductivity and/or the hydraulic head on the variances of the predictive head and the solute flux in nonstationary
flow fields. The study results show that the conditional calculation will significantly reduce the head variance. Since the
hydraulic head measurement points are treated as the interior boundary (Dirichlet boundary) conditions, conditioning on both
the hydraulic conductivity and the head measurements is much better than conditioning only on conductivity measurements for
reduction of head variance. However, for solute flux, variance reduction by the conditional study is not so significant. 相似文献
6.
随机地震反演关键参数优选和效果分析(英文) 总被引：2，自引：0，他引：2
随机地震反演技术是将地质统计理论和地震反演相结合的反演方法,它将地震资料、测井资料和地质统计学信息融合为地下模型的后验概率分布,利用马尔科夫链蒙特卡洛(MCMC)方法对该后验概率分布采样,通过综合分析多个采样结果来研究后验概率分布的性质,进而认识地下情况。本文首先介绍了随机地震反演的原理,然后对影响随机地震反演效果的四个关键参数,即地震资料信噪比、变差函数、后验概率分布的样本个数和井网密度进行分析并给出其优化原则。资料分析表明地震资料信噪比控制地震资料和地质统计规律对反演结果的约束程度,变差函数影响反演结果的平滑程度,后验概率分布的样本个数决定样本统计特征的可靠性,而参与反演的井网密度则影响反演的不确定性。最后通过对比试验工区随机地震反演和基于模型的确定性地震反演结果,指出随机地震反演可以给出更符合地下实际情况的模型。 相似文献
7.
3D stochastic inversion of magnetic data 总被引：1，自引：0，他引：1
8.
The coupled flow-mass transport inverse problem is formulated using the maximum likelihood estimation concept. An evolutionary computational algorithm, the genetic algorithm, is applied to search for a global or near-global solution. The resulting inverse model allows for flow and transport parameter estimation, based on inversion of spatial and temporal distributions of head and concentration measurements. Numerical experiments using a subset of the three-dimensional tracer tests conducted at the Columbus, Mississippi site are presented to test the model's ability to identify a wide range of parameters and parametrization schemes. The results indicate that the model can be applied to identify zoned parameters of hydraulic conductivity, geostatistical parameters of the hydraulic conductivity field, angle of hydraulic conductivity anisotropy, solute hydrodynamic dispersivity, and sorption parameters. The identification criterion, or objective function residual, is shown to decrease significantly as the complexity of the hydraulic conductivity parametrization is increased. Predictive modeling using the estimated parameters indicated that the geostatistical hydraulic conductivity distribution scheme produced good agreement between simulated and observed heads and concentrations. The genetic algorithm, while providing apparently robust solutions, is found to be considerably less efficient computationally than a quasi-Newton algorithm. 相似文献
9.
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
10.
Detecting and quantifying sources of non-stationarity via experimental semivariogram modeling 总被引：1，自引：0，他引：1
Miguel A. Cuba Oy Leuangthong Julian M. Ortiz 《Stochastic Environmental Research and Risk Assessment (SERRA)》2012,26(2):247-260
Conventional geostatistics often relies on the assumption of second order stationarity of the random function (RF). Generally,
local means and local variances of the random variables (RVs) are assumed to be constant throughout the domain. Large scale
differences in the local means and local variances of the RVs are referred to as trends. Two problems of building geostatistical
models in presence of mean trends are: (1) inflation of the conditional variances and (2) the spatial continuity is exaggerated.
Variance trends on the other hand cause conditional variances to be over-estimated in certain regions of the domain and under-estimated
in other areas. In both cases the uncertainty characterized by the geostatistical model is improperly assessed. This paper
proposes a new approach to identify the presence and contribution of mean and variance trends in the domain via calculation
of the experimental semivariogram. The traditional experimental semivariogram expression is decomposed into three components:
(1) the mean trend, (2) the variance trend and (3) the stationary component. Under stationary conditions, both the mean and
the variance trend components should be close to zero. This proposed approach is intended to be used in the early stages of
data analysis when domains are being defined or to verify the impact of detrending techniques in the conditioning dataset
for validating domains. This approach determines the source of a trend, thereby facilitating the choice of a suitable detrending
method for effective resource modeling. 相似文献
11.
Identifying the spatial distribution of hydrological properties of aquifers is a key problem in subsurface hydrology. The aquifer structure plays an important role in contaminant transport. Identifying the properties (primarily the hydraulic conductivity) is essentially an inversion problem that is ill-posed, non-unique and computationally intensive by definition. In this work, the non-uniqueness of the inverse problem is tackled via a novel Genetic Algorithm approach combined with a geostatistical method (Sequential Indicator Simulations) for construction of realizations of properties spatial distributions, which are modeled as random. The Genetic Algorithm cross-over operator is based on a novel concept of pilot-planes: daughter realizations adopt pilot-planes from one of their parents. In addition, each aquifer realization is conditioned on the geological hard data and is constructed by sampling the facies distribution, evaluated by indicator variograms. The approach is illustrated in two test cases: a synthetic two-dimensional (2D) case and an actual three-dimensional (3D) case. The results have shown the ability of the proposed approach to generate a set of realizations, where each individual exhibits minor deviations from the measurements. Further, a comparison between the proposed approach and direct (Monte Carlo) approach shows that the Genetic Algorithm was able to generate an ensemble of solutions with a better fitting of the measurements than the direct approach by a significantly reduced computational effort. 相似文献
12.
Geostatistical seismic inversion for non‐stationary patterns using direct sequential simulation and co‐simulation 
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下载免费PDF全文 Hamid Sabeti Ali Moradzadeh Faramarz Doulati Ardejani Leonardo Azevedo Amilcar Soares Pedro Pereira Ruben Nunes 《Geophysical Prospecting》2017,65(Z1):25-48
Geostatistical seismic inversion methods are routinely used in reservoir characterisation studies because of their potential to infer the spatial distribution of the petro‐elastic properties of interest (e.g., density, elastic, and acoustic impedance) along with the associated spatial uncertainty. Within the geostatistical seismic inversion framework, the retrieved inverse elastic models are conditioned by a global probability distribution function and a global spatial continuity model as estimated from the available well‐log data for the entire inversion grid. However, the spatial distribution of the real subsurface elastic properties is complex, heterogeneous, and, in many cases, non‐stationary since they directly depend on the subsurface geology, i.e., the spatial distribution of the facies of interest. In these complex geological settings, the application of a single distribution function and a spatial continuity model is not enough to properly model the natural variability of the elastic properties of interest. In this study, we propose a three‐dimensional geostatistical inversion technique that is able to incorporate the reservoir's heterogeneities. This method uses a traditional geostatistical seismic inversion conditioned by local multi‐distribution functions and spatial continuity models under non‐stationary conditions. The procedure of the proposed methodology is based on a zonation criterion along the vertical direction of the reservoir grid. Each zone can be defined by conventional seismic interpretation, with the identification of the main seismic units and significant variations of seismic amplitudes. The proposed method was applied to a highly non‐stationary synthetic seismic dataset with different levels of noise. The results of this work clearly show the advantages of the proposed method against conventional geostatistical seismic inversion procedures. It is important to highlight the impact of this technique in terms of higher convergence between real and inverted reflection seismic data and the more realistic approximation towards the real subsurface geology comparing with traditional techniques. 相似文献
13.
A nested workflow of multiple‐point geostatistics (MPG) and sequential Gaussian simulation (SGS) was tested on a study area of 6 km2 located about 20 km northwest of Quebec City, Canada. In order to assess its geological and hydrogeological parameter heterogeneity and to provide tools to evaluate uncertainties in aquifer management, direct and indirect field measurements are used as inputs in the geostatistical simulations to reproduce large and small‐scale heterogeneities. To do so, the lithological information is first associated to equivalent hydrogeological facies (hydrofacies) according to hydraulic properties measured at several wells. Then, heterogeneous hydrofacies (HF) realizations are generated using a prior geological model as training image (TI) with the MPG algorithm. The hydraulic conductivity (K) heterogeneity modeling within each HF is finally computed using SGS algorithm. Different K models are integrated in a finite‐element hydrogeological model to calculate multiple transport simulations. Different scenarios exhibit variations in mass transport path and dispersion associated with the large‐ and small‐scale heterogeneity respectively. Three‐dimensional maps showing the probability of overpassing different thresholds are presented as examples of management tools. 相似文献
14.
Comparison of Hydraulic Tomography with Traditional Methods at a Highly Heterogeneous Site 下载免费PDF全文
下载免费PDF全文 Over the past several decades, different groundwater modeling approaches of various complexities and data use have been developed. A recently developed approach for mapping hydraulic conductivity (K) and specific storage (Ss) heterogeneity is hydraulic tomography, the performance of which has not been compared to other more “traditional” methods that have been utilized over the past several decades. In this study, we compare seven methods of modeling heterogeneity which are (1) kriging, (2) effective parameter models, (3) transition probability/Markov Chain geostatistics models, (4) geological models, (5) stochastic inverse models conditioned to local K data, (6) hydraulic tomography, and (7) hydraulic tomography conditioned to local K data using data collected in five boreholes at a field site on the University of Waterloo (UW) campus, in Waterloo, Ontario, Canada. The performance of each heterogeneity model is first assessed during model calibration. In particular, the correspondence between simulated and observed drawdowns is assessed using the mean absolute error norm, (L1), mean square error norm (L2), and correlation coefficient (R) as well as through scatterplots. We also assess the various models on their ability to predict drawdown data not used in the calibration effort from nine pumping tests. Results reveal that hydraulic tomography is best able to reproduce these tests in terms of the smallest discrepancy and highest correlation between simulated and observed drawdowns. However, conditioning of hydraulic tomography results with permeameter K data caused a slight deterioration in accuracy of drawdown predictions which suggests that data integration may need to be conducted carefully. 相似文献
15.
J.A. Morakinyo R. Mackay 《Stochastic Environmental Research and Risk Assessment (SERRA)》2006,20(1-2):106-118
The pattern of ground contamination across a site depends on the historical pattern of contaminant releases at the surface
and the redistribution and the fate of contaminants below the surface. Using these concepts a new site assessment approach
(assessment modelling) is proposed based on the development of three stochastic models: a model of the physical structure
of the ground materials beneath the site; a model of the distribution of surface contaminant spills; and a model of the flow
and transport of spilled material into the heterogeneous underlying ground to construct alternative, equally likely, present
day contaminant distributions. Combining the models within a Monte Carlo framework can, in principle, improve the understanding
of the potential for excess contamination across the site and improve decisions on remediation options and locations. A trial
application has been undertaken in the UK using a particular site to assess the approach. The conditions at the site used
for the trial and the first of the stochastic model developments, the geostatistical modelling of the soil heterogeneity,
are presented in this paper. Non-parametric and parametric geostatistics have been employed to formulate the geostatistical
models of the site soils using lithological information from 146 trial pits and boreholes. The approach to the soil modelling
and the verification and validation of the results are described. The heterogeneity of the subsurface is complicated by the
presence of made-ground, comprised of various inert building wastes, and the non-stationarity of the heterogeneity of the
natural ground. This paper is the first of three describing the assessment modelling methodology and its trial application
to the site. 相似文献
16.
Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea 总被引：2，自引：0，他引：2
Dario Grana Enrico Paparozzi Silvia Mancini Cristiano Tarchiani 《Geophysical Prospecting》2013,61(3):613-629
In this paper we present a case history of seismic reservoir characterization where we estimate the probability of facies from seismic data and simulate a set of reservoir models honouring seismically‐derived probabilistic information. In appraisal and development phases, seismic data have a key role in reservoir characterization and static reservoir modelling, as in most of the cases seismic data are the only information available far away from the wells. However seismic data do not provide any direct measurements of reservoir properties, which have then to be estimated as a solution of a joint inverse problem. For this reason, we show the application of a complete workflow for static reservoir modelling where seismic data are integrated to derive probability volumes of facies and reservoir properties to condition reservoir geostatistical simulations. The studied case is a clastic reservoir in the Barents Sea, where a complete data set of well logs from five wells and a set of partial‐stacked seismic data are available. The multi‐property workflow is based on seismic inversion, petrophysics and rock physics modelling. In particular, log‐facies are defined on the basis of sedimentological information, petrophysical properties and also their elastic response. The link between petrophysical and elastic attributes is preserved by introducing a rock‐physics model in the inversion methodology. Finally, the uncertainty in the reservoir model is represented by multiple geostatistical realizations. The main result of this workflow is a set of facies realizations and associated rock properties that honour, within a fixed tolerance, seismic and well log data and assess the uncertainty associated with reservoir modelling. 相似文献
17.
Application of Large‐Scale Inversion Algorithms to Hydraulic Tomography in an Alluvial Aquifer 下载免费PDF全文
下载免费PDF全文 P. Fischer A. Jardani A. Soueid Ahmed M. Abbas X. Wang H. Jourde N. Lecoq 《Ground water》2017,55(2):208-218
Large‐scale inversion methods have been recently developed and permitted now to considerably reduce the computation time and memory needed for inversions of models with a large amount of parameters and data. In this work, we have applied a deterministic geostatistical inversion algorithm to a hydraulic tomography investigation conducted in an experimental field site situated within an alluvial aquifer in Southern France. This application aims to achieve a 2‐D large‐scale modeling of the spatial transmissivity distribution of the site. The inversion algorithm uses a quasi‐Newton iterative process based on a Bayesian approach. We compared the results obtained by using three different methodologies for sensitivity analysis: an adjoint‐state method, a finite‐difference method, and a principal component geostatistical approach (PCGA). The PCGA is a large‐scale adapted method which was developed for inversions with a large number of parameters by using an approximation of the covariance matrix, and by avoiding the calculation of the full Jacobian sensitivity matrix. We reconstructed high‐resolution transmissivity fields (composed of up to 25,600 cells) which generated good correlations between the measured and computed hydraulic heads. In particular, we show that, by combining the PCGA inversion method and the hydraulic tomography method, we are able to substantially reduce the computation time of the inversions, while still producing high‐quality inversion results as those obtained from the other sensitivity analysis methodologies. 相似文献
18.
Joint AVO inversion, wavelet estimation and noise-level estimation using a spatially coupled hierarchical Bayesian model 总被引：1，自引：0，他引：1
The main objective of the AVO inversion is to obtain posterior distributions for P-wave velocity, S-wave velocity and density from specified prior distributions, seismic data and well-log data. The inversion problem also involves estimation of a seismic wavelet and the seismic-noise level. The noise model is represented by a zero mean Gaussian distribution specified by a covariance matrix. A method for joint AVO inversion, wavelet estimation and estimation of the noise level is developed in a Bayesian framework. The stochastic model includes uncertainty of both the elastic parameters, the wavelet, and the seismic and well-log data. The posterior distribution is explored by Markov-chain Monte-Carlo simulation using the Gibbs' sampler algorithm. The inversion algorithm has been tested on a seismic line from the Heidrun Field with two wells located on the line. The use of a coloured seismic-noise model resulted in about 10% lower uncertainties for the P-wave velocity, S-wave velocity and density compared with a white-noise model. The uncertainty of the estimated wavelet is low. In the Heidrun example, the effect of including uncertainty of the wavelet and the noise level was marginal with respect to the AVO inversion results. 相似文献
19.
In geostatistical inverse modeling, hydrogeological parameters, such as hydraulic conductivity, are estimated as spatial fields. Upon discretization this results in several thousand (log-)hydraulic conductivity values to be estimated. Common inversion schemes rely on gradient-based parameter estimation methods which require the sensitivity of all measurements with respect to all parameters. Point-like measurements of steady-state concentration in aquifers are generally not well suited for gradient-based methods, because typical plumes exhibit only a very narrow fringe at which the concentration decreases from a maximal value to zero. Only here the sensitivity of concentration with respect to hydraulic conductivity significantly differs from zero. Thus, if point-like measurements of steady-state concentration do not lie in this narrow fringe, their sensitivity with respect to hydraulic conductivity is zero. Observations of concentrations averaged over a larger control volume, by contrast, show a more regular sensitivity pattern. We thus suggest artificially increasing the sampling volume of steady-state concentration measurements for the evaluation of sensitivities in early stages of an iterative parameter estimation scheme. We present criteria for the extent of artificially increasing the sampling volume and for decreasing it when the simulation results converge to the measurements. By this procedure, we achieve high stability in geostatistical inversion of steady-state concentration measurements. The uncertainty of the estimated parameter fields is evaluated by generating conditional realizations. 相似文献
20.
Inverse modeling is a useful tool in ground water flow modeling studies. The most frequent difficulties encountered when using this technique are the lack of conditioning information (e.g., heads and transmissivities), the uncertainty in available data, and the nonuniqueness of the solution. These problems can be addressed and quantified through a stochastic Monte Carlo approach. The aim of this work was to compare the applicability of two stochastic inverse modeling approaches in a field-scale application. The multi-scaling (MS) approach uses a downscaling parameterization procedure that is not based on geostatistics. The pilot point (PP) approach uses geostatistical random fields as initial transmissivity values and an experimental variogram to condition the calibration. The studied area (375 km2) is part of a regional aquifer, northwest of Montreal in the St. Lawrence lowlands (southern Québec). It is located in limestone, dolomite, and sandstone formations, and is mostly a fractured porous medium. The MS approach generated small errors on heads, but the calibrated transmissivity fields did not reproduce the variogram of observed transmissivities. The PP approach generated larger errors on heads but better reproduced the spatial structure of observed transmissivities. The PP approach was also less sensitive to uncertainty in head measurements. If reliable heads are available but no transmissivities are measured, the MS approach provides useful results. If reliable transmissivities with a well inferred spatial structure are available, then the PP approach is a better alternative. This approach however must be used with caution if measured transmissivities are not reliable. 相似文献