共查询到20条相似文献，搜索用时 25 毫秒
1.
Solving Speed and Memory Issues in Multiple-Point Statistics Simulation Program SNESIM 总被引：2，自引：2，他引：0
In the last 10 years, Multiple-Point Statistics (MPS) modeling has emerged in Geostatistics as a valuable alternative to traditional variogram-based and object-based modeling. In contrast to variogram-based simulation, which is limited to two-point correlation reproduction, MPS simulation extracts and reproduces multiple-point statistics moments from training images; this allows modeling geologically realistic features, such as channels that control reservoir connectivity and flow behavior. In addition, MPS simulation works on individual pixels or small groups of pixels (patterns), thus does not suffer from the same data conditioning limitations as object-based simulation. The Single Normal Equation Simulation program SNESIM was the first implementation of MPS simulation to propose, through the introduction of search trees, an efficient solution to the extraction and storage of multiple-point statistics moments from training images. SNESIM is able to simulate three-dimensional models; however, memory and speed issues can occur when applying it to multimillion cell grids. Several other implementations of MPS simulation were proposed after SNESIM, but most of them manage to reduce memory demand or simulation time only at the expense of data conditioning exactitude and/or training pattern reproduction quality. In this paper, the original SNESIM program is revisited, and solutions are presented to eliminate both memory demand and simulation time limitations. First, we demonstrate that the time needed to simulate a grid node is a direct function of the number of uninformed locations in the conditioning data search neighborhood. Thus, two improvements are proposed to maximize the ratio of informed to uniformed locations in search neighborhoods: a new multiple-grid approach introducing additional intermediary subgrids; and a new search neighborhood designing process to preferentially include previously simulated node locations. Finally, because SNESIM memory demand and simulation time increase with the size of the data template used to extract multiple-point statistics moments from the training image and build the search tree, a simple method is described to minimize data template sizes while preserving training pattern reproduction quality. 相似文献
2.
L. Y. Hu Y. Liu C. Scheepens A. W. Shultz R. D. Thompson 《Mathematical Geosciences》2014,46(2):227-240
The multiple-point simulation (MPS) method has been increasingly used to describe the complex geologic features of petroleum reservoirs. The MPS method is based on multiple-point statistics from training images that represent geologic patterns of the reservoir heterogeneity. The traditional MPS algorithm, however, requires the training images to be stationary in space, although the spatial distribution of geologic patterns/features is usually nonstationary. Building geologically realistic but statistically stationary training images is somehow contradictory for reservoir modelers. In recent research on MPS, the concept of a training image has been widely extended. The MPS approach is no longer restricted by the size or the stationarity of training images; a training image can be a small geometrical element or a full-field reservoir model. In this paper, the different types of training images and their corresponding MPS algorithms are first reviewed. Then focus is placed on a case where a reservoir model exists, but needs to be conditioned to well data. The existing model can be built by process-based, object-based, or any other type of reservoir modeling approach. In general, the geologic patterns in a reservoir model are constrained by depositional environment, seismic data, or other trend maps. Thus, they are nonstationary, in the sense that they are location dependent. A new MPS algorithm is proposed that can use any existing model as training image and condition it to well data. In particular, this algorithm is a practical solution for conditioning geologic-process-based reservoir models to well data. 相似文献
3.
多点地质统计学建模的发展趋势 总被引：2，自引：0，他引：2
从算法研究、训练图像处理和实际应用三个方面详细解剖了国内外多点地质统计学的发展历程,在此基础上,分析了多点地质统计学主流的几种算法的核心原理、适用范围及优缺点,以此来对储层建模的发展趋势作出展望。目前,多点地质统计学虽是随机建模的一种前沿研究热点,但由于其尚未成熟,仍需对建模算法进行研究。为此,在前人研究的基础上,重点分析了多点地质统计学的发展趋势：合理处理训练图像;合理利用软信息;选择合适的相似性方法;选择合适的标准化方法;合理利用平稳性;算法间的耦合;选择合适的过滤器;拓展缝洞型碳酸盐岩模拟。最后,提出多点地质统计学在储层建模方面,应从增加储层的模拟区域、提高模拟精度、扩大储层相的模拟范围和提高计算机模拟效率等方面进行改进。 相似文献
4.
在储层建模中利用多点地质统计学整合地质概念模型及其解释(英文) 总被引：1，自引：0，他引：1
基于三维空间中稀疏的观测数据,地质学家和储层建模人员尝试预测井间的地质沉积相的空间非均质性时,地质概念模型和先验认识在其中扮演着重要的角色。这种整合先验模型或解释的过程有时是隐蔽或不易察觉的,正如在手工绘等值线图中的情形;它也能够被显式地运用到某种算法当中,比如数字绘图中的算法。新近兴起的多点地质统计学为地质学家和储层建模人员提供了一种有力工具,它强调使用训练图像把先验模型明确而定量地引入到储层建模当中。先验地质模型包含了被研究的真实储层中确信存在的样式,而训练图像则是该模型的定量化表达。通过再现高阶统计量,多点算法能够从训练图像中捕捉复杂的(非线性)特征样式并把它们锚定到观测的井位数据。文中描述了多点地质统计学原理,以突出训练图像概念重要性为主线,描述了多点地质统计学在建立三维储层模型中的应用。 相似文献
5.
The reproduction of the non-stationary distribution and detailed characteristics of geological bodies is the main difficulty of reservoir modeling. Recently developed multiple-point geostatistics can represent a stationary geological body more effectively than traditional methods. When restricted to a stationary hypothesis, multiple-point geostatistical methods cannot simulate a non-stationary geological body effectively, especially when using non-stationary training images (TIs). According to geologic principles, the non-stationary distribution of geological bodies is controlled by a sedimentary model. Therefore, in this paper, we propose auxiliary variables based on the sedimentary model, namely geological vector information (GVI). GVI can characterize the non-stationary distribution of TIs and simulation domains before sequential simulation, and the precision of data event statistics will be enhanced by the sequential simulation’s data event search area limitations under the guidance of GVI. Consequently, the reproduction of non-stationary geological bodies will be improved. The key features of this method are as follows: (1) obtain TIs and geological vector information for simulated areas restricted by sedimentary models; (2) truncate TIs into a number of sub-TIs using a set of cut-off values such that each sub-TI is stationary and the adjacent sub-TIs have a certain similarity; (3) truncate the simulation domain into a number of sub-regions with the same cut-off values used in TI truncation, so that each sub-region corresponds to a number of sub-TIs; (4) use an improved method to scan the TI or TIs and construct a single search tree to restore replicates of data events located in different sub-TIs; and (5) use an improved conditional probability distribution function to perform sequential simulation. A FORTRAN program is implemented based on the SNESIM. 相似文献
6.
利用三维地质模拟技术重构地质现象的三维空间分布,是实现自然资源管理和风险评估的重要基础和前提。多点统计学方法通过探寻多点间的空间结构关系,结合随机模拟方法生成具有差异性的模拟结果,较好地再现了复杂的地质现象。然而,如何构建合适、有效的训练图像一直是基于多点统计学三维地质模拟的核心问题。本文提出了一种改进的多点统计学算法。本方法结合了序贯模拟和迭代的方法,将二维剖面扩展为三维训练图像,再结合EM-Like算法,实现了三维地质结构的优化模拟。建模实例结果表明,本方法能确保训练图像对内部模拟网格的约束,准确模拟研究区的地层层序,并很好地再现二维地质剖面所反映的地层结构关系。 相似文献
7.
Indicator Simulation Accounting for Multiple-Point Statistics 总被引：7，自引：0，他引：7
Geostatistical simulation aims at reproducing the variability of the real underlying phenomena. When nonlinear features or large-range connectivity is present, the traditional variogram-based simulation approaches do not provide good reproduction of those features. Connectivity of high and low values is often critical for grades in a mineral deposit. Multiple-point statistics can help to characterize these features. The use of multiple-point statistics in geostatistical simulation was proposed more than 10 years ago, on the basis of the use of training images to extract the statistics. This paper proposes the use of multiple-point statistics extracted from actual data. A method is developed to simulate continuous variables. The indicator kriging probabilities used in sequential indicator simulation are modified by probabilities extracted from multiple-point configurations. The correction is done under the assumption of conditional independence. The practical implementation of the method is illustrated with data from a porphyry copper mine. 相似文献
8.
从精细油藏描述中地质建模的意义和现状入手,介绍了多点地质统计学建模研究现状及其与传统地质建模方法的差
异。以辽河盆地西部凹陷某蒸汽驱试验区为例,分析了多点地质统计学建模中建模的基础、训练图像的建立、多点地质统计学
建模与传统地质建模相比所具有的优势等内容。指出多点地质统计学在井间预测方面具有明显优于其他传统建模方法的特
点。在文献调研基础上,结合自身工作实践,探讨了多点地质统计学建模目前存在的问题和未来的发展方向。指出未来多点
地质统计学建模的发展方向主要包括多信息综合地质成因分析基础上的训练图像获取、多点地质统计学算法进行改进和完善
和多点地质统计学建模方法应用领域的扩大等。 相似文献
9.
Snehamoy Chatterjee Hussein Mustapha Roussos Dimitrakopoulos 《Computational Geosciences》2016,20(3):399-420
Spatial uncertainty modelling is a complex and challenging job for orebody modelling in mining, reservoir characterization in petroleum, and contamination modelling in air and water. Stochastic simulation algorithms are popular methods for such modelling. In this paper, discrete wavelet transformation (DWT)-based multiple point simulation algorithm for continuous variable is proposed that handles multi-scale spatial characteristics in datasets and training images. The DWT of a training image provides multi-scale high-frequency wavelet images and one low-frequency scaling image at the coarsest scale. The simulation of the proposed approach is performed on the frequency (wavelet) domain where the scaling image and wavelet images across the scale are simulated jointly. The inverse DWT reconstructs simulated realizations of an attribute of interest in the space domain. An automatic scale-selection algorithm using dominant mode difference is applied for the selection of the optimal scale of wavelet decomposition. The proposed algorithm reduces the computational time required for simulating large domain as compared to spatial domain multi-point simulation algorithm. The algorithm is tested with an exhaustive dataset using conditional and unconditional simulation in two- and three-dimensional fluvial reservoir and mining blasted rock data. The realizations generated by the proposed algorithm perform well and reproduce the statistics of the training image. The study conducted comparing the spatial domain filtersim multiple-point simulation algorithm suggests that the proposed algorithm generates equally good realizations at lower computational cost. 相似文献
10.
Libing Wang 《Mathematical Geology》1996,28(7):895-907
Large-scale reservoir architecture constitutes first-order reservoir heterogeneity and dietates to a large extent reservoir flow behavior. It also manifests geometric characteristics beyond the capability of traditional geostatistical models conditioned only on single-point and two-point statistics. Multiple-point statistics, as obtained by scanning a training image deemed representative of the actual reservoir, if reproduced properly provides stochastic models that better capture the essence of the heterogeneity. A growth algorithm, coupled with an optimization procedure, is proposed to reproduce target multiple-point histograms. The growth algorithm makes an analogy between geological accretion process and stochastic process and amounts to restricting the random path of sequential simulation at any given stage to a set of eligible nodes (immediately adjacent to a previously simulated node or sand grain). The proposed algorithm, combined with a multiple-grid approach, is shown to reproduce effectively the geometric essence of complex training images exhibiting long-range and curvilinear structures. Also, by avoiding a rigorous search for global minimum and accepting local minima, the proposed algorithm improves CPU time over traditional optimization procedures by several orders of magnitude. Average flow responses run on simulated realizations are shown to bracket correctly the reference responses of the training image. 相似文献
11.
12.
多点地质统计学是一种建立地质模型的统计学方法,该方法比传统的两点地质统计学更适合河流相沉积体系。本文综合地震、测井、录井及生产资料,绘制了长庆油田苏里格气田苏48区块盒8下段辫状河训练图像,并在此基础上利用多点地质统计学方法,加入三维地震资料作为约束,以水平井整体开发为研究对象,建立了该区的地质模型,优化了水平井整体部署,指导水平井导向。利用多口水平井实钻效果验证了地质模型的精度,以苏19-62井为例得到:录井显示气层钻遇率为78.3%,测井显示气层钻遇率为69%。这与地震反演剖面预测相似,且与实钻效果有很好的对应性。 相似文献
13.
Conditional Simulation with Patterns 总被引：17，自引：0，他引：17
An entirely new approach to stochastic simulation is proposed through the direct simulation of patterns. Unlike pixel-based
(single grid cells) or object-based stochastic simulation, pattern-based simulation simulates by pasting patterns directly
onto the simulation grid. A pattern is a multi-pixel configuration identifying a meaningful entity (a puzzle piece) of the
underlying spatial continuity. The methodology relies on the use of a training image from which the pattern set (database)
is extracted. The use of training images is not new. The concept of a training image is extensively used in simulating Markov
random fields or for sequentially simulating structures using multiple-point statistics. Both these approaches rely on extracting
statistics from the training image, then reproducing these statistics in multiple stochastic realizations, at the same time
conditioning to any available data. The proposed approach does not rely, explicitly, on either a statistical or probabilistic
methodology. Instead, a sequential simulation method is proposed that borrows heavily from the pattern recognition literature
and simulates by pasting at each visited location along a random path a pattern that is compatible with the available local
data and any previously simulated patterns. This paper discusses the various implementation details to accomplish this idea.
Several 2D illustrative as well as realistic and complex 3D examples are presented to showcase the versatility of the proposed
algorithm. 相似文献
14.
Validation Techniques for Geological Patterns Simulations Based on Variogram and Multiple-Point Statistics 总被引：2，自引：2，他引：0
Traditional simulation methods that are based on some form of kriging are not sensitive to the presence of strings of connectivity
of low or high values. They are particularly inappropriate in many earth sciences applications, where the geological structures
to be simulated are curvilinear. In such cases, techniques allowing the reproduction of multiple-point statistics are required.
The aim of this paper is to point out the advantages of integrating such multiple-statistics in a model in order to allow
shape reproduction, as well as heterogeneity structures, of complex geological patterns to emerge. A comparison between a
traditional variogram-based simulation algorithm, such as the sequential indicator simulation, and a multiple-point statistics
algorithm (e.g., the single normal equation simulation) is presented. In particular, it is shown that the spatial distribution
of limestone with meandering channels in Lecce, Italy is better reproduced by using the latter algorithm. The strengths of
this study are, first, the use of a training image that is not a fluvial system and, more importantly, the quantitative comparison
between the two algorithms. The paper focuses on different metrics that facilitate the comparison of the methods used for
limestone spatial distribution simulation: both objective measures of similarity of facies realizations and high-order spatial
cumulants based on different third- and fourth-order spatial templates are considered. 相似文献
15.
The Necessity of a Multiple-Point Prior Model 总被引：9，自引：0，他引：9
Any interpolation, any hand contouring or digital drawing of a map or a numerical model necessarily calls for a prior model
of the multiple-point statistics that link together the data to the unsampled nodes, then these unsampled nodes together.
That prior model can be implicit, poorly defined as in hand contouring; it can be explicit through an algorithm as in digital
mapping. The multiple-point statistics involved go well beyond single-point histogram and two-point covariance models; the
challenge is to define algorithms that can control more of such statistics, particularly those that impact most the utilization
of the resulting maps beyond their visual appearance. The newly introduced multiple-point simulation (mps) algorithms borrow
the high order statistics from a visually and statistically explicit model, a training image. It is shown that mps can simulate
realizations with high entropy character as well as traditional Gaussian-based algorithms, while offering the flexibility
of considering alternative training images with various levels of low entropy (organized) structures. The impact on flow performance
(spatial connectivity) of choosing a wrong training image among many sharing the same histogram and variogram is demonstrated. 相似文献
16.
Luis Manuel de Vries Jesus Carrera Oriol Falivene Oscar Gratacós Luit Jan Slooten 《Mathematical Geosciences》2009,41(1):29-42
Simulation of flow and solute transport through aquifers or oil reservoirs requires a precise representation of subsurface
heterogeneity that can be achieved by stochastic simulation approaches. Traditional geostatistical methods based on variograms,
such as truncated Gaussian simulation or sequential indicator simulation, may fail to generate the complex, curvilinear, continuous
and interconnected facies distributions that are often encountered in real geological media, due to their reliance on two-point
statistics. Multiple Point Geostatistics (MPG) overcomes this constraint by using more complex point configurations whose
statistics are retrieved from training images. Obtaining representative statistics requires stationary training images, but
geological understanding often suggests a priori facies variability patterns. This research aims at extending MPG to non-stationary
facies distributions. The proposed method subdivides the training images into different areas. The statistics for each area
are stored in separate frequency search trees. Several training images are used to ensure that the obtained statistics are
representative. The facies probability distribution for each cell during simulation is calculated by weighting the probabilities
from the frequency trees. The method is tested on two different object-based training image sets. Results show that non-stationary
training images can be used to generate suitable non-stationary facies distributions. 相似文献
17.
Direct Multiple-Point Geostatistical Simulation of Edge Properties for Modeling Thin Irregularly Shaped Surfaces 总被引：3，自引：1，他引：2
Thin, irregularly shaped surfaces such as clay drapes often have a major control on flow and transport in heterogeneous porous
media. Clay drapes are often complex, curvilinear three-dimensional surfaces and display a very complex spatial distribution.
Variogram-based stochastic approaches are also often not able to describe the spatial distribution of clay drapes since complex,
curvilinear, continuous, and interconnected structures cannot be characterized using only two-point statistics. Multiple-point
geostatistics aims to overcome the limitations of the variogram. The premise of multiple-point geostatistics is to move beyond
two-point correlations between variables and to obtain (cross) correlation moments at three or more locations at a time using
training images to characterize the patterns of geological heterogeneity. Multiple-point geostatistics can reproduce thin
irregularly shaped surfaces such as clay drapes, but this is often computationally very intensive. This paper describes and
applies a methodology to simulate thin, irregularly shaped surfaces with a smaller CPU and RAM demand than the conventional
multiple-point statistical methods. The proposed method uses edge properties for indicating the presence of thin irregularly
shaped surfaces. Instead of pixel values, edge properties indicating the presence of irregularly shaped surfaces are simulated
using snesim. This method allows direct simulation of edge properties instead of pixel properties to make it possible to perform multiple-point
geostatistical simulations with a larger cell size and thus a smaller computation time and memory demand. This method is particularly
valuable for three-dimensional applications of multiple-point geostatistics. 相似文献
18.
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
19.
基于Google Earth软件建立曲流河地质知识库 总被引：8，自引：1，他引：7
在对密井网解剖、露头解剖、现代沉积解剖、沉积模拟实验等建立地质知识库方法优缺点分析基础上,利用Google Earth软件测量了一系列曲流河道的基础数据,结合“将今论古”思想,提出了基于Google Earth软件建立曲流河地质知识库的方法。首先,分析了已有方法的优缺点。其次,介绍了Google Earth软件测量曲流河道的方法步骤,并测量了不同地区不同曲率的河道宽度、点坝长度及弧长,得到了一系列测量数据,建立了测量数据表,并结合已有经验公式与测量数据拟合得到的公式综合分析。结果表明,不同环境中曲流河的河道宽度和点坝长度具不同的相关关系,曲流河的地质知识库不能按统一标准考虑。在不同曲率情况下,河道宽度和点坝长度之间的相关关系亦不同,且随着曲率的减少,其相关关系减弱,反映出点坝的发育程度受河流弯曲程度控制。最后,建议采用建立地质模式库的思想建立定量化曲流河地质知识库,用于对储层建模进行有效约束。 相似文献
20.
In order to determine to what extent a spatial random field can be characterized by its low-order distributions, we consider
four models (specifically, random spatial tessellations) with exactly the same univariate and bivariate distributions and
we compare the statistics associated with various multiple-point configurations and the responses to specific transfer functions.
The three- and four-point statistics are found to be the same or experimentally hardly distinguishable because of ergodic
fluctuations, whereas change of support and flow simulation produce very different outcomes. This example indicates that low-order
distributions may not discriminate between contending random field models, that simulation algorithms based on such distributions
may not reproduce the spatial properties of a given model or training image, and that the inference of high-order distribution
may require very large training images. 相似文献