共查询到20条相似文献，搜索用时 31 毫秒
1.
Wenlong Xu 《Mathematical Geology》1996,28(7):937-949
In geology, structures displaying differing local directions of continuity are widespread, a typical example being a flusial depositional system. Conventional pixel-based geostatistical algorithms, may fail to reproduce such curvilinear structures. Conversely, object-based algorithms can reproduce curvilinear shapes but are difficult to condition to dense local data. Local depositional directions as obtained from dipmeter data. 3D seismic data, and geological interpretation represent critical information. An improved pixel-based geostatistical algorithm is proposed to account for such directional information. Case studies demonstrate the potential and limitations of the algorithm. 相似文献
2.
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
3.
ODSIM: An Object-Distance Simulation Method for Conditioning Complex Natural Structures 总被引：1，自引：1，他引：0
Stochastic simulation of categorical objects is traditionally achieved either with object-based or pixel-based methods. Whereas
object-based modeling provides realistic results but raises data conditioning problems, pixel-based modeling provides exact
data conditioning but may lose some features of the simulated objects such as connectivity. We suggest a hybrid dual-scale
approach to combine both shape realism and strict data conditioning. The procedure combines the distance transform to a skeleton
object representing coarse-scale structures, plus a classical pixel-based random field and threshold representing fine-scale
features. This object-distance simulation method (ODSIM) uses a perturbed distance to objects and is particularly appropriate
for modeling structures related to faults or fractures such as karsts, late dolomitized rocks, and mineralized veins. We demonstrate
this method to simulate dolomite geometry and discuss strategies to apply this method more generally to simulate binary shapes. 相似文献
4.
To assess differences between object and pixel-based reservoir modeling techniques, ten realizations of a UK Continental Shelf braided fluvial reservoir were produced using Boolean Simulation (BS) and Sequential Indicator Simulation (SIS). Various sensitivities associated with geological input data as well as with technique-specific modeling parameters were analyzed for both techniques. The resulting realizations from the object-based and pixel-based modeling efforts were assessed by visual inspection and by evaluation of the values and ranges of the single-phase effective permeability tensors, obtained through upscaling. The BS method performed well for the modeling of two types of fluvial channels, yielding well-confined channels, but failed to represent the complex interaction of these with sheetflood and other deposits present in the reservoir. SIS gave less confined channels and had great difficulty in representing the large-scale geometries of one type of channel while maintaining its appropriate proportions. Adding an SIS background to the Boolean channels, as opposed to a Boolean background, resulted in an improved distribution of sheetflood bodies. The permeability results indicated that the SIS method yielded models with much higher horizontal permeability values (20–100%) and lower horizontal anisotropy than the BS versions. By widening the channel distribution and increasing the range of azimuths, however, the BS-produced models gave results approaching the SIS behavior. For this reservoir, we chose to combine the two methods by using object-based channels and a pixel-based heterogeneous background, resulting in moderate permeability and anisotropy levels. 相似文献
5.
比较岩性模型建立方法。首先,在高分辨率层序地层学的指导下,最大限度地应用地质、露头、三维地震、测井等静态资料,发挥井点资料垂向分辨率高,地震资料横向信息丰富的优势,在地质规律约束下建立不同时间的高精度等时地层格架模型。然后,在精细格架模型的基础上,以测井解释得到的岩相数据作为条件数据,分别采用指示克里格、截断高斯模拟、Object-modeling算法、贯指示模拟建立砂体展布模型。最后,通过抽稀检验评价不同算法对模拟结果的影响,实现算法及其参数的优选,从而指导整个区块不同开发阶段,不同井网密度时全区三维精细地质模型的建立,也可为具有相似地质环境的油田建立三维地质模型提供参考。通过比较,优选出指示克里格、序贯指示模拟两种算法都能较好表征本研究区地质情况。 相似文献
6.
The variogram is a critical input to geostatistical studies: (1) it is a tool to investigate and quantify the spatial variability of the phenomenon under study, and (2) most geostatistical estimation or simulation algorithms require an analytical variogram model, which they will reproduce with statistical fluctuations. In the construction of numerical models, the variogram reflects some of our understanding of the geometry and continuity of the variable, and can have a very important impact on predictions from such numerical models. The principles of variogram modeling are developed and illustrated with a number of practical examples. A three-dimensional interpretation of the variogram is necessary to fully describe geologic continuity. Directional continuity must be described simultaneously to be consistent with principles of geological deposition and for a legitimate measure of spatial variability for geostatistical modeling algorithms. Interpretation principles are discussed in detail. Variograms are modeled with particular functions for reasons of mathematical consistency. Used correctly, such variogram models account for the experimental data, geological interpretation, and analogue information. The steps in this essential data integration exercise are described in detail through the introduction of a rigorous methodology. 相似文献
7.
Teacher''s Aide Variogram Interpretation and Modeling 总被引：13，自引：0，他引：13
The variogram is a critical input to geostatistical studies: (1) it is a tool to investigate and quantify the spatial variability of the phenomenon under study, and (2) most geostatistical estimation or simulation algorithms require an analytical variogram model, which they will reproduce with statistical fluctuations. In the construction of numerical models, the variogram reflects some of our understanding of the geometry and continuity of the variable, and can have a very important impact on predictions from such numerical models. The principles of variogram modeling are developed and illustrated with a number of practical examples. A three-dimensional interpretation of the variogram is necessary to fully describe geologic continuity. Directional continuity must be described simultaneously to be consistent with principles of geological deposition and for a legitimate measure of spatial variability for geostatistical modeling algorithms. Interpretation principles are discussed in detail. Variograms are modeled with particular functions for reasons of mathematical consistency. Used correctly, such variogram models account for the experimental data, geological interpretation, and analogue information. The steps in this essential data integration exercise are described in detail through the introduction of a rigorous methodology. 相似文献
8.
Simulation of mineral grades with hard and soft conditioning data: application to a porphyry copper deposit 总被引：1，自引：0，他引：1
This work deals with the geostatistical simulation of mineral grades whose distribution exhibits spatial trends within the
ore deposit. It is suggested that these trends can be reproduced by using a stationary random field model and by conditioning
the realizations to data that incorporate the available information on the local grade distribution. These can be hard data
(e.g., assays on samples) or soft data (e.g., rock-type information) that account for expert geological knowledge and supply
the lack of hard data in scarcely sampled areas. Two algorithms are proposed, depending on the kind of soft data under consideration:
interval constraints or local moment constraints. An application to a porphyry copper deposit is presented, in which it is
shown that the incorporation of soft conditioning data associated with the prevailing rock type improves the modeling of the
uncertainty in the actual copper grades. 相似文献
9.
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
10.
Extended Probability Perturbation Method for Calibrating Stochastic Reservoir Models 总被引：1，自引：0，他引：1
Lin Y. Hu 《Mathematical Geosciences》2008,40(8):875-885
Calibrating a stochastic reservoir model on large, fine-grid to hydrodynamic data requires consistent methods to modify the
petrophysical properties of the model. Several methods have been developed to address this problem. Recent methods include
the Gradual Deformation Method (GDM) and the Probability Perturbation Method (PPM). The GDM has been applied to pixel-based
models of continuous and categorical variables, as well as object-based models. Initially, the PPM has been applied to pixel-based
models of categorical variables generated by sequential simulation. In addition, the PPM relies on an analytical formula (known
as the tau-model) to approximate conditional probabilities. In this paper, an extension of the PPM to any type of probability
distributions (discrete, continuous, or mixed) is presented. This extension is still constrained by the approximation using
the tau-model. However, when applying the method to white noises, this approximation is no longer necessary. The result is
an entirely new and rigorous method for perturbing any type of stochastic models, a modified PPM employed in similar manner
to the GDM. 相似文献
11.
Maximilian Huber Fabrizio Marconi Massimiliano Moscatelli 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2015,9(1):49-56
The evaluation of the underground soil stratigraphy is a key aspect in geotechnical site characterisation. However, these means of site exploration are only pinholing subsoil conditions and expert knowledge is needed to understand subsoil conditions in order to build a reliable geological-geotechnical model. This contribution employs a geostatistical simulation methodology for the simulation of random fields representing geological uncertainty. This combines borehole data and expert knowledge via a mathematical framework. Moreover a risk-based site characterisation scheme is developed for urban site characterisation. This novel characterisation scheme offers additional insight into the effects of large-scale, geological spatial variability by using fragility curves to quantify these effects. 相似文献
12.
Kernel Principal Component Analysis for Efficient,Differentiable Parameterization of Multipoint Geostatistics 总被引：6，自引：5，他引：1
This paper describes a novel approach for creating an efficient, general, and differentiable parameterization of large-scale
non-Gaussian, non-stationary random fields (represented by multipoint geostatistics) that is capable of reproducing complex
geological structures such as channels. Such parameterizations are appropriate for use with gradient-based algorithms applied
to, for example, history-matching or uncertainty propagation. It is known that the standard Karhunen–Loeve (K–L) expansion,
also called linear principal component analysis or PCA, can be used as a differentiable parameterization of input random fields
defining the geological model. The standard K–L model is, however, limited in two respects. It requires an eigen-decomposition
of the covariance matrix of the random field, which is prohibitively expensive for large models. In addition, it preserves
only the two-point statistics of a random field, which is insufficient for reproducing complex structures.
In this work, kernel PCA is applied to address the limitations associated with the standard K–L expansion. Although widely
used in machine learning applications, it does not appear to have found any application for geological model parameterization.
With kernel PCA, an eigen-decomposition of a small matrix called the kernel matrix is performed instead of the full covariance
matrix. The method is much more efficient than the standard K–L procedure. Through use of higher order polynomial kernels,
which implicitly define a high-dimensionality feature space, kernel PCA further enables the preservation of high-order statistics
of the random field, instead of just two-point statistics as in the K–L method. The kernel PCA eigen-decomposition proceeds
using a set of realizations created by geostatistical simulation (honoring two-point or multipoint statistics) rather than
the analytical covariance function. We demonstrate that kernel PCA is capable of generating differentiable parameterizations
that reproduce the essential features of complex geological structures represented by multipoint geostatistics. The kernel
PCA representation is then applied to history match a water flooding problem. This example demonstrates that kernel PCA can
be used with gradient-based history matching to provide models that match production history while maintaining multipoint
geostatistics consistent with the underlying training image. 相似文献
13.
Alessandro Comunian Leonardo De Micheli Claudio Lazzati Fabrizio Felletti Francesca Giacobbo Mauro Giudici Riccardo Bersezio 《Hydrogeology Journal》2016,24(2):319-334
The fine-scale heterogeneity of porous media affects the large-scale transport of solutes and contaminants in groundwater and it can be reproduced by means of several geostatistical simulation tools. However, including the available geological information in these tools is often cumbersome. A hierarchical simulation procedure based on a binary tree is proposed and tested on two real-world blocks of alluvial sediments, of a few cubic meters volume, that represent small-scale aquifer analogs. The procedure is implemented using the sequential indicator simulation, but it is so general that it can be adapted to various geostatistical simulation tools, improving their capability to incorporate geological information, i.e., the sedimentological and architectural characterization of heterogeneity. When compared with a standard sequential indicator approach on bi-dimensional simulations, in terms of proportions and connectivity indicators, the proposed procedure yields reliable results, closer to the reference observations. Different ensembles of three-dimensional simulations based on different hierarchical sequences are used to perform numerical experiments of conservative solute transport and to obtain ensembles of equivalent pore velocity and dispersion coefficient at the scale length of the blocks (meter). Their statistics are used to estimate the impact of the variability of the transport properties of the simulated blocks on contaminant transport modeled on bigger domains (hectometer). This is investigated with a one-dimensional transport modeling based on the Kolmogorov-Dmitriev theory of branching stochastic processes. Applying the proposed approach with diverse binary trees and different simulation settings provides a great flexibility, which is revealed by the differences in the breakthrough curves. 相似文献
14.
本文系统地介绍了多点地质统计学的基本原理及方法，并以我国渤海湾盆地某区块新近系明化镇组河流相储层为例，进行了多点统计学随机建模的实例分析。多点地质统计学为储层随机建模的国际前沿研究方向，该方法综合了基于象元的方法易忠实条件数据以及基于目标的方法易再现目标几何形态的优点，同时克服了传统基于变差函数的二点统计学不能表达复杂空间结构和再现目标几何形态的不足。通过理论与实例研究，分析了目前多点统计学尚存在的问题(包括训练图像平稳性问题、目标连续性问题以及综合软信息的问题等)及未来发展的方向。 相似文献
15.
精细油藏描述中的沉积微相建模进展 总被引：1，自引：0，他引：1
从区域沉积学分析和运用高分辨率层序地层学建立地层分层数据库两方面入手,分析了精细油藏描述中沉积微相建模研究的基础.结合建模实践,详细介绍了当前沉积微相建模中广泛使用的利用地质、地球物理、油田开发动态数据等信息基于目标和基于象元的各种随机建模方法和构型分析法、井间地震等建模新技术,并对各种方法和技术的优缺点进行了简评.在此基础上分析了沉积微相研究中应注意的建模方法技术、建模软件的选择、相模型的验证和优选等问题.针对目前的研究现状和存在的问题,指出了精细油藏描述中沉积微相建模研究的发展趋势. 相似文献
16.
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
17.
Landslide susceptibility assessment using object mapping units,decision tree,and support vector machine models in the Three Gorges of China 总被引：4，自引：3，他引：1
Due to the particular geographical location and complex geological conditions, the Three Gorges of China suffer from many landslide hazards that often result in tragic loss of life and economic devastation. To reduce the casualty and damages, an effective and accurate method of assessing landslide susceptibility is necessary. Object-based data mining methods were applied to a case study of landslide susceptibility assessment on the Guojiaba Town of the Three Gorges. The study area was partitioned into object mapping units derived from 30 m resolution Landsat TM images using multi-resolution segmentation algorithm based on the landslide factors of engineering rock group, homogeneity, and reservoir water level. Landslide locations were determined by interpretation of Landsat TM images and extensive field surveys. Eleven primary landslide-related factors were extracted from the topographic and geologic maps, and satellite images. Those factors were selected as independent variables using significance testing and correlation coefficient analysis, including slope, profile curvature, engineering rock group, slope structure, distance from faults, land cover, tasseled cap transformation wetness index, reservoir water level, homogeneity, and first and second principal components of the images. Decision tree and support vector machine (SVM) models with the optimal parameters were trained and then used to map landslide susceptibility, respectively. The analytical results were validated by comparing them with known landslides using the success rate and prediction rate curves and classification accuracy. The object-based SVM model has the highest correct rate of 89.36 % and a kappa coefficient of 0.8286 and outperforms the pixel-based SVM, object-based C5.0, and pixel-based SVM models. 相似文献
18.
The advent of multiple-point geostatistics (MPS) gave rise to the integration of complex subsurface geological structures
and features into the model by the concept of training images. Initial algorithms generate geologically realistic realizations
by using these training images to obtain conditional probabilities needed in a stochastic simulation framework. More recent
pattern-based geostatistical algorithms attempt to improve the accuracy of the training image pattern reproduction. In these
approaches, the training image is used to construct a pattern database. Consequently, sequential simulation will be carried
out by selecting a pattern from the database and pasting it onto the simulation grid. One of the shortcomings of the present
algorithms is the lack of a unifying framework for classifying and modeling the patterns from the training image. In this
paper, an entirely different approach will be taken toward geostatistical modeling. A novel, principled and unified technique
for pattern analysis and generation that ensures computational efficiency and enables a straightforward incorporation of domain
knowledge will be presented. 相似文献
19.
An important issue in reservoir modeling is accurate generation of complex structures. The problem is difficult because the connectivity of the flow paths must be preserved. Multiple-point geostatistics is one of the most effective methods that can model the spatial patterns of geological structures, which is based on an informative geological training image that contains the variability, connectivity, and structural properties of a reservoir. Several pixel- and pattern-based methods have been developed in the past. In particular, pattern-based algorithms have become popular due to their ability for honoring the connectivity and geological features of a reservoir. But a shortcoming of such methods is that they require a massive data base, which make them highly memory- and CPU-intensive. In this paper, we propose a novel methodology for which there is no need to construct pattern data base and small data event. A new function for the similarity of the generated pattern and the training image, based on a cross-correlation (CC) function, is proposed that can be used with both categorical and continuous training images. We combine the CC function with an overlap strategy and a new approach, adaptive recursive template splitting along a raster path, in order to develop an algorithm, which we call cross-correlation simulation (CCSIM), for generation of the realizations of a reservoir with accurate conditioning and continuity. The performance of CCSIM is tested for a variety of training images. The results, when compared with those of the previous methods, indicate significant improvement in the CPU and memory requirements. 相似文献
20.
Methods reported in the literature for rock fracture simulations include approaches based on stochastic geometry, multiple-point
statistics and a combination of geostatistics for fracture density and object-based modelling for fracture geometries. The
advantages and disadvantages of each of these approaches are discussed with examples. By way of review, the authors begin
with the geostatistical indicator simulation method, based on the truncated–Gaussian algorithm; this is followed by multiple-point
statistical simulation and then the stochastic geometry approach, which is based on marked point process simulation. A new
approach, based on pluriGaussian structural simulation, is then introduced. The new approach incorporates in the simulation
the spatial correlation between different sets of fractures, which in general, is very difficult, if not impossible, to accomplish
in the three methods reviewed. Each simulation method is summarised together with detailed simulation procedures for each.
A published two-dimensional fracture dataset is used as a means of assessing the performance of each simulation method and
of demonstrating the concepts discussed in the text. 相似文献