共查询到20条相似文献，搜索用时 31 毫秒
1.
Conditioning complex subsurface flow models on nonlinear data is complicated by the need to preserve the expected geological connectivity patterns to maintain solution plausibility. Generative adversarial networks (GANs) have recently been proposed as a promising approach for low-dimensional representation of complex high-dimensional images. The method has also been adopted for low-rank parameterization of complex geologic models to facilitate uncertainty quantification workflows. A difficulty in adopting these methods for subsurface flow modeling is the complexity associated with nonlinear flow data conditioning. While conditional GAN (CGAN) can condition simulated images on labels, application to subsurface problems requires efficient conditioning workflows for nonlinear data, which is far more complex. We present two approaches for generating flow-conditioned models with complex spatial patterns using GAN. The first method is through conditional GAN, whereby a production response label is used as an auxiliary input during the training stage of GAN. The production label is derived from clustering of the flow responses of the prior model realizations (i.e., training data). The underlying assumption of this approach is that GAN can learn the association between the spatial features corresponding to the production responses within each cluster. An alternative method is to use a subset of samples from the training data that are within a certain distance from the observed flow responses and use them as training data within GAN to generate new model realizations. In this case, GAN is not required to learn the nonlinear relation between production responses and spatial patterns. Instead, it is tasked to learn the patterns in the selected realizations that provide a close match to the observed data. The conditional low-dimensional parameterization for complex geologic models with diverse spatial features (i.e., when multiple geologic scenarios are plausible) performed by GAN allows for exploring the spatial variability in the conditional realizations, which can be critical for decision-making. We present and discuss the important properties of GAN for data conditioning using several examples with increasing complexity.
相似文献2.
Two-dimensional Conditional Simulations Based on the Wavelet Decomposition of Training Images 总被引：2，自引：1，他引：1
Scale dependency is a critical topic when modeling spatial phenomena of complex geological patterns that interact at different
spatial scales. A two-dimensional conditional simulation based on wavelet decomposition is proposed for simulating geological
patterns at different scales. The method utilizes the wavelet transform of a training image to decompose it into wavelet coefficients
at different scales, and then quantifies their spatial dependence. Joint simulation of the wavelet coefficients is used together
with available hard and or soft conditioning data. The conditionally co-simulated wavelet coefficients are back-transformed
generating a realization of the attribute under study. Realizations generated using the proposed method reproduce the conditioning
data, the wavelet coefficients and their spatial dependence. Two examples using geological images as training images elucidate
the different aspects of the method, including hard and soft conditioning, the ability to reproduce some non-linear features
and scale dependencies of the training images. 相似文献
3.
Tobias Lochbühler Guillaume Pirot Julien Straubhaar Niklas Linde 《Mathematical Geosciences》2014,46(5):625-645
Geophysical tomography captures the spatial distribution of the underlying geophysical property at a relatively high resolution, but the tomographic images tend to be blurred representations of reality and generally fail to reproduce sharp interfaces. Such models may cause significant bias when taken as a basis for predictive flow and transport modeling and are unsuitable for uncertainty assessment. We present a methodology in which tomograms are used to condition multiple-point statistics (MPS) simulations. A large set of geologically reasonable facies realizations and their corresponding synthetically calculated cross-hole radar tomograms are used as a training image. The training image is scanned with a direct sampling algorithm for patterns in the conditioning tomogram, while accounting for the spatially varying resolution of the tomograms. In a post-processing step, only those conditional simulations that predicted the radar traveltimes within the expected data error levels are accepted. The methodology is demonstrated on a two-facies example featuring channels and an aquifer analog of alluvial sedimentary structures with five facies. For both cases, MPS simulations exhibit the sharp interfaces and the geological patterns found in the training image. Compared to unconditioned MPS simulations, the uncertainty in transport predictions is markedly decreased for simulations conditioned to tomograms. As an improvement to other approaches relying on classical smoothness-constrained geophysical tomography, the proposed method allows for: (1) reproduction of sharp interfaces, (2) incorporation of realistic geological constraints and (3) generation of multiple realizations that enables uncertainty assessment. 相似文献
4.
We present a general, flexible, and fast neural network approach to the modeling of a conditional distribution of a discrete random variable, given a continuous or discrete random vector. Although many more applications of the neural net technique could be envisioned, the aim is to apply the developed methodology to the integration of seismic data into reservoir models. Many geostatistical methods for integrating seismic data rely on a screening assumption of further away seismic events by the colocated seismic datum. Such assumption makes the task of modeling cross-covariances and local conditional distributions much easier. In many cases, however, the seismic data exhibit distinct and locally varying spatial patterns of continuity related to geological events such as channels, shale bodies, or fractures. The previous screening assumption prevents recognizing and hence utilizing these patterns of seismic data. In this paper we propose to relate seismic data to facies or petrophysical properties through a colocated window of seismic information instead of the single colocated seismic datum. The variation of seismic data from one window to another is accounted for. Several examples demonstrate that using such a window improves the predictive power of seismic data. 相似文献
5.
Anne Randi Syversveen Ragnar Hauge Jan Inge Tollefsrud Ulf Lægreid Alister MacDonald 《Mathematical Geosciences》2011,43(7):763-781
We present an approach for modeling facies bodies in which a highly constrained stochastic object model is used to integrate
detailed seismic interpretation of the reservoir’s sedimentological architecture directly in a three-dimensional reservoir
model. The approach fills the gap between the use of seismic data in a true deterministic sense, in which the facies body
top and base are resolved and mapped directly, and stochastic methods in which the relationship between seismic attributes
and facies is defined by conditional probabilities. The lateral geometry of the facies bodies is controlled by seismic interpretations
on horizon slices or by direct body extraction, whereas facies body thickness and cross-sectional shape are defined by a mixture
of seismic data, well data, and user defined object shapes. The stochastic terms in the model are used to incorporate local
geometric variability, which is used to increase the geological realism of the facies bodies and allow for correct, flexible
well conditioning. The result is a set of three-dimensional facies bodies that are constrained to the seismic interpretations
and well data. Each body is defined as a parametric object that includes information such as location of the body axis, depositional
direction, axis-to-margin normals, and external body geometry. The parametric information is useful for defining geologically
realistic intrabody petrophysical trends and for controlling connectivity between stacked facies bodies. 相似文献
6.
2007年国际石油地质统计学大会的成果表明,运用地质研究、地震解释、生产动态三方面的数据并与模型相结合,是当前油气储层建模理论和应用的一个发展趋势。油藏描述、油藏表征及储层建模发展的整个过程,始终体现了这种多学科的融合。由于地质统计学的促进,储层建模技术具备了分析和处理各种主要由地下地质环境引起的不确定性的能力。地质研究对储层建模的核心作用主要体现为相控建模原则的确立和地质概念模型的应用上。最后,研究了储层建模中地震数据的参与和生产动态数据的结合等方面的发展。 相似文献
7.
Transitive Geostatistics for Stepwise Modeling Across Boundaries between Rock Regions 总被引：2，自引：1，他引：1
J. A. Vargas-Guzmán 《Mathematical Geosciences》2008,40(8):861-873
A single intrinsic stationary random field may not account for transitional heterogeneity and abrupt dissimilarity of geological
properties across boundaries between rock type regions. This paper proposes the stepwise construction of transitive covariance
models for modeling continuous properties correlated across boundaries of multiple disjoint physical domains such as rock
type bodies. Modeling in geology is usually simplified by splitting the geological space into rock type geo-domains (e.g.,
strata, sedimentary facies, soil series, diagenetic regions and alteration zones). Due to the limitations of simultaneous
solutions, a simplification is to model each domain independently at the cost of losing the conditioning of properties across
domains. This paper proposes to organize the modeling process in a triangular array which follows events in the geological
time domain; for example, the younger formations are at the top of the pyramid and the older formation at the base. The estimation
may go from top to base by assuming that younger events have perturbed older formations. Geology shows the scars of events
that cumulate in rock formations before they are finally eroded. In some cases, older formations may be parent material for
younger formations. The continuous property within each geo-domain has a conditional covariance in the main diagonal of the
array which may belong to a specific event in the geological time. This sequence leads to transitive estimation and simulations
in the physical space. If a simultaneous solution is sought (i.e., the future and past are correlated both ways), the complex
covariance functions can be constructed stepwise from conditional spectra. 相似文献
8.
Multiple-point statistics (MPS) provides a flexible grid-based approach for simulating complex geologic patterns that contain high-order statistical information represented by a conceptual prior geologic model known as a training image (TI). While MPS is quite powerful for describing complex geologic facies connectivity, conditioning the simulation results on flow measurements that have a nonlinear and complex relation with the facies distribution is quite challenging. Here, an adaptive flow-conditioning method is proposed that uses a flow-data feedback mechanism to simulate facies models from a prior TI. The adaptive conditioning is implemented as a stochastic optimization algorithm that involves an initial exploration stage to find the promising regions of the search space, followed by a more focused search of the identified regions in the second stage. To guide the search strategy, a facies probability map that summarizes the common features of the accepted models in previous iterations is constructed to provide conditioning information about facies occurrence in each grid block. The constructed facies probability map is then incorporated as soft data into the single normal equation simulation (snesim) algorithm to generate a new candidate solution for the next iteration. As the optimization iterations progress, the initial facies probability map is gradually updated using the most recently accepted iterate. This conditioning process can be interpreted as a stochastic optimization algorithm with memory where the new models are proposed based on the history of the successful past iterations. The application of this adaptive conditioning approach is extended to the case where multiple training images are proposed as alternative geologic scenarios. The advantages and limitations of the proposed adaptive conditioning scheme are discussed and numerical experiments from fluvial channel formations are used to compare its performance with non-adaptive conditioning techniques. 相似文献
9.
在油田开发方案设计阶段,基础地质资料尤其是钻井资料相对较少,因此对储层分布的认识具有较大的不确定性。沉积相建模为储层分布不确定性的定量表征提供了技术手段,但每种沉积相建模方法具有各自的适应性。以渤海P油田为例,分别应用布尔模拟和示性点过程模拟方法,以河道带和河道内砂体为描述对象建立了2种沉积相模型,定量表征2种储层分布模式,分析2种模拟结果的储层分布规律,指出以河道内砂体为描述对象的建模方法提供了该油田储层分布的最可能模式,而以河道带为描述对象的建模方法提供了储层分布的另一种可能模式,为油藏数值模拟方案设计和敏感性分析提供了地质依据。 相似文献
10.
Using Sequential Indicator Simulation as a Tool in Reservoir Description: Issues and Uncertainties 总被引：3，自引：0，他引：3
Flow simulation studies require an accurate model of the reservoir in terms of its sedimentological architecture. Pixel-based reservoir modeling techniques are often used to model this architecture. There are, however, two problem areas with such techniques. First, several statistical parameters have to be provided whose influence on the resulting model is not readily inferable. Second, conditioning the models to relevant geological data that carry great uncertainty on their own adds to the difficulty of obtaining reliable models and assessing model reliability. The Sequential Indicator Simulation (SIS) method has been used to examine the impact of such uncertainties on the final reservoir model. The effects of varying variogram types, frequencies of lithology occurrence, and the gridblock model orientation with respect to the sedimentological trends are illustrated using different reservoir modeling studies. Results indicate, for example, that the choice of variogram type can have a significant impact on the facies model. Also, reproduction of sedimentological trends and large geometries requires careful parameter selection. By choosing the appropriate modeling strategy, sedimentological principles can be translated into the numerical model. Solutions for dealing with such issues and the geological uncertainties are presented. In conclusion, each reservoir modeling study should begin by developing a thorough quantitative sedimentological understanding of the reservoir under study, followed by detailed sensitivity analyses of relevant statistical and geological parameters. 相似文献
11.
Training Images from Process-Imitating Methods 总被引：2，自引：2，他引：0
Alessandro Comunian Sanjeev K. Jha Beatrice M. S. Giambastiani Gregoire Mariethoz Bryce F. J. Kelly 《Mathematical Geosciences》2014,46(2):241-260
The lack of a suitable training image is one of the main limitations of the application of multiple-point statistics (MPS) for the characterization of heterogeneity in real case studies. Process-imitating facies modeling techniques can potentially provide training images. However, the parameterization of these process-imitating techniques is not straightforward. Moreover, reproducing the resulting heterogeneous patterns with standard MPS can be challenging. Here the statistical properties of the paleoclimatic data set are used to select the best parameter sets for the process-imitating methods. The data set is composed of 278 lithological logs drilled in the lower Namoi catchment, New South Wales, Australia. A good understanding of the hydrogeological connectivity of this aquifer is needed to tackle groundwater management issues. The spatial variability of the facies within the lithological logs and calculated models is measured using fractal dimension, transition probability, and vertical facies proportion. To accommodate the vertical proportions trend of the data set, four different training images are simulated. The grain size is simulated alongside the lithological codes and used as an auxiliary variable in the direct sampling implementation of MPS. In this way, one can obtain conditional MPS simulations that preserve the quality and the realism of the training images simulated with the process-imitating method. The main outcome of this study is the possibility of obtaining MPS simulations that respect the statistical properties observed in the real data set and honor the observed conditioning data, while preserving the complex heterogeneity generated by the process-imitating method. In addition, it is demonstrated that an equilibrium of good fit among all the statistical properties of the data set should be considered when selecting a suitable set of parameters for the process-imitating simulations. 相似文献
12.
Characterization of complex geological features and patterns remains one of the most challenging tasks in geostatistics. Multiple point statistics (MPS) simulation offers an alternative to accomplish this aim by going beyond classical two-point statistics. Reproduction of features in the final realizations is achieved by borrowing high-order spatial statistics from a training image. Most MPS algorithms use one training image at a time chosen by the geomodeler. This paper proposes the use of multiple training images simultaneously for spatial modeling through a scheme of data integration for conditional probabilities known as a linear opinion pool. The training images (TIs) are based on the available information and not on conceptual geological models; one image comes from modeling the categories by a deterministic approach and another comes from the application of conventional sequential indicator simulation. The first is too continuous and the second too random. The mixing of TIs requires weights for each of them. A methodology for calibrating the weights based on the available drillholes is proposed. A measure of multipoint entropy along the drillholes is matched by the combination of the two TIs. The proposed methodology reproduces geologic features from both TIs with the correct amount of continuity and variability. There is no need for a conceptual training image from another modeling technique; the data-driven TIs permit a robust inference of spatial structure from reasonably spaced drillhole data. 相似文献
13.
14.
多点统计地质建模技术自提出至今已有20余年的历史,已经成为储集层地质建模的国际前沿研究方向,在理论和应用研究方面都取得了长足进展。以多点统计地质建模技术的发展历程为主线,以多点统计地质建模技术的技术进展为核心,论述了多点统计地质建模技术的研究进展,对主要的多点统计地质建模方法进行了分类,系统讨论了具有发展潜力的多点统计地质建模方法的原理、特点以及存在的问题,并以扎格罗斯盆地孔隙型碳酸盐岩油藏S油藏为例进行了应用研究,对比了多点统计模拟与序贯指示模拟的优劣。研究表明,多点统计模拟在复杂的相模拟方面,较序贯指示模拟具有明显的优势;基于图型的Dispat方法采用图型替换数据事件的策略,使相的分布规律更符合地质学家的地质认识。这一认识为孔隙型碳酸盐岩油藏建模提供了一种新思路,对类似油藏的地质建模具有借鉴作用。 相似文献
15.
16.
基于深度学习的地震数据噪声压制方法是当前地震数据去噪处理的重要方向。深度学习方法突破了传统滤波处理的局限,在对常规地震数据的噪声压制中表现出效率高、信噪分离效果好的特点。但针对深部弱有效反射数据,当前的深度学习方法特征提取能力有限,难以取得较好的去噪效果。笔者等结合深反射地震数据特点,针对当前深度学习噪声压制方法在特征提取及对数据集依赖上的局限,提出了基于注意力循环生成对抗网络(Attention Cycle- Consistent Generative Adversarial Networks,A- CGAN)的深反射地震数据随机噪声压制方法。借助循环一致生成对抗网络(Cycle- Consistent Generative Adversarial Networks,Cycle- GAN)的域映射思想,降低对数据集的要求。为了构建适用于深反射地震数据的去噪网络,从3个方面对Cycle- GAN进行改进：在Cycle- GAN的生成器（去噪器）中加入残差结构和注意力机制,用于加深网络深度和提高其特征提取能力;在Cycle- GAN的鉴别器中使用块判决,提高鉴别精度和准确度;在损失函数部分加入感知一致性损失函数,提升网络模型恢复纹理细节信息的能力。通过合成地震数据和实际深反射地震数据测试,验证了优化算法的有效性,体现了良好的应用价值。 相似文献
17.
基于深度学习的地震数据噪声压制方法是当前地震数据去噪处理的重要方向。深度学习方法突破了传统滤波处理的局限,在对常规地震数据的噪声压制中表现出效率高、信噪分离效果好的特点。但针对深部弱有效反射数据,当前的深度学习方法特征提取能力有限,难以取得较好的去噪效果。笔者等结合深反射地震数据特点,针对当前深度学习噪声压制方法在特征提取及对数据集依赖上的局限,提出了基于注意力循环生成对抗网络(Attention Cycle- Consistent Generative Adversarial Networks,A- CGAN)的深反射地震数据随机噪声压制方法。借助循环一致生成对抗网络(Cycle- Consistent Generative Adversarial Networks,Cycle- GAN)的域映射思想,降低对数据集的要求。为了构建适用于深反射地震数据的去噪网络,从3个方面对Cycle- GAN进行改进：在Cycle- GAN的生成器（去噪器）中加入残差结构和注意力机制,用于加深网络深度和提高其特征提取能力;在Cycle- GAN的鉴别器中使用块判决,提高鉴别精度和准确度;在损失函数部分加入感知一致性损失函数,提升网络模型恢复纹理细节信息的能力。通过合成地震数据和实际深反射地震数据测试,验证了优化算法的有效性,体现了良好的应用价值。 相似文献
18.
BassamFAIBassam 《中国地质大学学报(英文版)》2003,14(3):283-292
Gengma region, Sanjiang district is known to have some large-scale gold deposits. GIS predictive model for hydroghermal gold potential was carried out in this region using weights of evidence modeling technique. Datasets used include large-scale hydroghermal gold deposit records, geological, geophysical and remote sensing imagery. Based on the geological and mineral characteristics of areas with known gold occurrences in Sanjiang, several geological features were thought to be indicative of areas with potential for the occurrence of hydroghtermal gold deposits. Indicative features were extracted from geoexploration datasets for use as input in the predictive model. The features include host rock lithology, geologic structures, wallrock alteration and associated (volcanic-plutonic) igneous rocks. To determine which of the indicative geological features are important spatial predictors of area with potential for gold deposits, spatial analysis was done through the modeling method. The input maps were buffered and the optimum distance of spatial association for each geological feature was determined by calculating the contrast and studentized contrast. Five feature maps were converted to binary predictor patterns and used as evidential layers for predictive modeling. The binary patterns were integrated in two combinations, each of which consists of four patterns in order to avoid over prediction due to the effect of duplicate features in the two structural evidences. The two produced potential maps define almost similar favorable zones. Areas of intersections between these zones in the two potential maps placed the highest predictive favorable zones in the region. 相似文献
19.
A simple example simulating a mixture of two normal populations results in some important observations, nonnormality and nonsymmetry of the mixture conditional pdf, nonlinearity of the conditional mean as a function of the conditioning data, heteroscedasticity of the conditional variance and its nonmonotonicity as a function of distance of the unknown to the conditioning data. A comparison of the mixture statistics with those predicted by traditional models ignoring the mixture reveals the inadequacy and inappropriateness of these traditional approaches. A mixture of two multivariate normal populations is illustrated through the analytical expressions of its conditional distribution and moments. 相似文献
20.
A simple example simulating a mixture of two normal populations results in some important observations, nonnormality and nonsymmetry of the mixture conditional pdf, nonlinearity of the conditional mean as a function of the conditioning data, heteroscedasticity of the conditional variance and its nonmonotonicity as a function of distance of the unknown to the conditioning data. A comparison of the mixture statistics with those predicted by traditional models ignoring the mixture reveals the inadequacy and inappropriateness of these traditional approaches. A mixture of two multivariate normal populations is illustrated through the analytical expressions of its conditional distribution and moments. 相似文献