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1.
Many hydrological, environmental, or engineering exploration tasks require predicting spatially continuous scenarios of sparsely measured borehole logging data. We present a methodology to probabilistically predict such scenarios constrained by ill-posed geophysical tomography. Our approach allows for transducing tomographic reconstruction ambiguity into the probabilistic prediction of spatially continuous target parameter scenarios. It is even applicable to data sets where petrophysical relations in the survey area are non-unique, i.e., different facies related petrophysical relations may be present. We employ static two-layer artificial neural networks (ANNs) for prediction and additionally evaluate, whether the training performance of the ANNs can be used to rank geophysical tomograms, which are mathematically equal reconstructions of physical parameter distributions in the ground. We illustrate our methodology using a realistic synthetic database for maximal control about the prediction performance and ranking potential of the approach. For doing so, we try to link geophysical radar and seismic tomography as input parameters to porosity of the ground as target parameter of ANN. However, the approach is flexible and can cope with any combination of geophysical tomograms and hydrologic, environmental or engineering target parameters. Ranking of equivalent geophysical tomograms based on additional borehole logging data is found to be generally possible, but risks remain that the ranking based on the ANN training performance does not fully coincide with the closeness of geophysical tomograms to ground truth. Since geophysical field data sets do usually not offer control options similar to those used in our synthetic database, we do not recommend the utilization of recurrent ANNs to learn weights for the individual geophysical tomograms used in the prediction procedure. 相似文献
2.
Piotr W. Mirowski Daniel M. Tetzlaff Roy C. Davies David S. McCormick Nneka Williams Claude Signer 《Mathematical Geosciences》2009,41(4):447-474
This research introduces a novel method to assess the validity of training images used as an input for Multipoint Geostatistics,
alternatively called Multiple Point Simulation (MPS). MPS are a family of spatial statistical interpolation algorithms that
are used to generate conditional simulations of property fields such as geological facies. They are able to honor absolute
“hard” constraints (e.g., borehole data) as well as “soft” constraints (e.g., probability fields derived from seismic data,
and rotation and scale). These algorithms require 2D or 3D training images or analogs whose textures represent a spatial arrangement
of geological properties that is presumed to be similar to that of a target volume to be modeled. To use the current generation
of MPS algorithms, statistically valid training image are required as input. In this context, “statistical validity” includes
a requirement of stationarity, so that one can derive from the training image an average template pattern. This research focuses
on a practical method to assess stationarity requirements for MPS algorithms, i.e., that statistical density or probability
distribution of the quantity shown on the image does not change spatially, and that the image shows repetitive shapes whose
orientation and scale are spatially constant. This method employs image-processing techniques based on measures of stationarity
of the category distribution, the directional (or orientation) property field and the scale property field of those images.
It was successfully tested on a set of two-dimensional images representing geological features and its predictions were compared
to actual realizations of MPS algorithms. An extension of the algorithms to 3D images is also proposed. As MPS algorithms
are being used increasingly in hydrocarbon reservoir modeling, the methods described should facilitate screening and selection
of the input training images. 相似文献
3.
Modeling Combined Geological and Grade Uncertainty: Application of Multiple-Point Simulation at the Apensu Gold Deposit, Ghana 总被引：1，自引：1，他引：0
Traditionally within the mining industry, single models for both grade and geology of orebodies are created upon which all mine development decisions are based. These models provide a single interpretation of the extent and continuity of the mineralization envelope based on solids and sections interpreted from relatively widely spaced drilling. The inherent variable behavior of grade and geology cannot be understood from a single estimated resource model. To account for uncertainty in the geology and mineralization envelope, Newmont Mining Corporation uses multiple-point statistics (MPS), an emerging spatial simulation framework, which can be employed to generate multiple, geologically realistic, realizations of data representing attributes of mineral deposits that display complex non-linear features. MPS uses a conceptual model of the geology, termed a training image, to infer these high-order spatial relationships. A detailed application of the MPS algorithm at the structurally controlled Apensu gold deposit, Ghana, demonstrates the practical intricacies of the MPS framework and documents efficiency and effectiveness. Multiple realizations of the Apensu deposit allow for an assessment of the geologic and volumetric uncertainty, which is further combined with grade simulations to generate a more complete picture of the true uncertainty of the deposit. 相似文献
4.
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
5.
6.
Application of multiple-point geostatistics on modelling groundwater flow and transport in a cross-bedded aquifer (Belgium) 总被引：3，自引：1，他引：2
Sedimentological processes often result in complex three-dimensional subsurface heterogeneity of hydrogeological parameter values. Variogram-based stochastic approaches are often not able to describe heterogeneity in such complex geological environments. This work shows how multiple-point geostatistics can be applied in a realistic hydrogeological application to determine the impact of complex geological heterogeneity on groundwater flow and transport. The approach is applied to a real aquifer in Belgium that exhibits a complex sedimentary heterogeneity and anisotropy. A training image is constructed based on geological and hydrogeological field data. Multiple-point statistics are borrowed from this training image to simulate hydrofacies occurrence, while intrafacies permeability variability is simulated using conventional variogram-based geostatistical methods. The simulated hydraulic conductivity realizations are used as input to a groundwater flow and transport model to investigate the effect of small-scale sedimentary heterogeneity on contaminant plume migration. Results show that small-scale sedimentary heterogeneity has a significant effect on contaminant transport in the studied aquifer. The uncertainty on the spatial facies distribution and intrafacies hydraulic conductivity distribution results in a significant uncertainty on the calculated concentration distribution. Comparison with standard variogram-based techniques shows that multiple-point geostatistics allow better reproduction of irregularly shaped low-permeability clay drapes that influence solute transport. 相似文献
7.
Uncertainties in Facies Proportion Estimation II: Application to Geostatistical Simulation of Facies and Assessment of Volumetric Uncertainties 总被引：1，自引：0，他引：1
Geostatistical simulations of lithotypes or facies are commonly used to create a geological model and to describe the heterogeneities of petroleum reservoirs. However, it is difficult to handle such models in the framework of multiple realizations to assess the uncertainty of hydrocarbon in place. Indeed, the hydrocarbon in place is correlated with the facies proportions, which are themselves uncertain. The uncertainty model of facies proportions is not easy to describe because of closure relationships. A previous attempt was made with a nonparametric approach using the resampling technique. It has been successful in a stationary case but it is difficult to extend it to nonstationary cases. In this paper, we have applied the vectorial beta parametric model or Dirichlet model. It has provided much more realistic uncertainties on volumetrics in very different geological and geostatistical contexts. 相似文献
8.
As additional multiple-point statistical (MPS) algorithms are developed, there is an increased need for scientific ways for comparison beyond the usual visual comparison or simple metrics, such as connectivity measures. In this paper, we start from the general observation that any (not just MPS) geostatistical simulation algorithm represents two types of variability: (1) the within-realization variability, namely, that realizations reproduce a spatial continuity model (variogram, Boolean, or training-image based), (2) the between-realization variability representing a model of spatial uncertainty. In this paper, it is argued that any comparison of algorithms needs, at a minimum, to be based on these two randomizations. In fact, for certain MPS algorithms, it is illustrated with different examples that there is often a trade-off: Increased pattern reproduction entails reduced spatial uncertainty. In this paper, the subjective choice that the best algorithm maximizes pattern reproduction is made while at the same time maximizes spatial uncertainty. The discussion is also limited to fairly standard multiple-point algorithms and that our method does not necessarily apply to more recent or possibly future developments. In order to render these fundamental principles quantitative, this paper relies on a distance-based measure for both within-realization variability (pattern reproduction) and between-realization variability (spatial uncertainty). It is illustrated in this paper that this method is efficient and effective for two-dimensional, three-dimensional, continuous, and discrete training images. 相似文献
9.
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
10.
冲积扇储层具有典型的非平稳分布特征,传统的基于平稳假设的地质统计建模方法无法准确模拟其沉积微相。在充 分分析其沉积微相分布特征的基础上,以克拉玛依油田三叠系克下组冲积扇储层为例,综合现代沉积、密井网、露头等信 息,应用基于地质矢量信息的多点地质统计学方法建立了精细、合理的储层沉积微相三维模型。首先,综合现代沉积特征 与地下储层沉积微相解剖成果,应用“模式指导、规模约束”的方法,建立符合研究区沉积模式和定量规模的训练图像和 基于冲积扇沉积体系的矢量坐标系统,表征了训练图像的地质矢量信息,提出基于预模拟实现的模拟域地质矢量信息表征 方法,建立了待模拟地质体的三维矢量信息模型;最后,采用基于矢量信息的多点地质统计方法建立了冲积扇储层沉积微 相模型,并采用多种标准对模拟实现进行可靠性分析。模拟结果表明冲积扇相带分布符合地质认识,沉积微相的形态、展 布特征、组合样式、规模与训练图像、地下沉积微相解剖认识相符。 相似文献
11.
The spatial continuity of facies is one of the key factors controlling flow in reservoir models. Traditional pixel-based methods
such as truncated Gaussian random fields and indicator simulation are based on only two-point statistics, which is insufficient
to capture complex facies structures. Current methods for multi-point statistics either lack a consistent statistical model
specification or are too computer intensive to be applicable. We propose a Markov mesh model based on generalized linear models
for geological facies modeling. The approach defines a consistent statistical model that is facilitated by efficient estimation
of model parameters and generation of realizations. Our presentation includes a formulation of the general framework, model
specifications in two and three dimensions, and details on how the parameters can be estimated from a training image. We illustrate
the method using multiple training images, including binary and trinary images and simulations in two and three dimensions.
We also do a thorough comparison to the snesim approach. We find that the current model formulation is applicable for multiple training images and compares favorably to
the snesim approach in our test examples. The method is highly memory efficient. 相似文献
12.
Comparison of three geostatistical methods for hydrofacies simulation: a test on alluvial sediments 总被引：3，自引：0，他引：3
Diana dell’Arciprete Riccardo Bersezio Fabrizio Felletti Mauro Giudici Alessandro Comunian Philippe Renard 《Hydrogeology Journal》2012,20(2):299-311
The hydrodispersive properties of porous sediments are strongly influenced by the heterogeneity at fine scales, which can be modeled by geostatistical simulations. In order to improve the assessment of the properties of three different geostatistical simulation methods (Sequential indicator simulation, SISIM; Transition probability geostatistical simulation, T-PROGS; Multiple point simulation, MPS) a comparison test at different scales was performed for a well-exposed aquifer analogue. In the analysed volume (approximately 30,000?m3) four operative hydrofacies have been recognised: very fine sand and silt, sand, gravelly sand and open framework gravel. Several equiprobable realizations were computed with SISIM, MPS and T-PROGS for a test volume of approximately 400?m3 and for the entire volume, and the different outcomes were compared with visual inspection and connectivity analysis of the very or poorly permeable structures. The comparison of the different simulations shows that the geological model is best reproduced when the simulations are realised separately for each highest rank depositional element and subsequently merged. Moreover, the three methods yield different images of the volume; in particular MPS is efficient in mapping the geometries of the most represented hydrofacies, whereas SISIM and T-PROGS can account for the distribution of the less represented facies. 相似文献
13.
14.
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
15.
A cluster analysis methodology is developed to recover facies realizations from observed reservoir attributes. A maximum likelihood
estimator allows us for identifying the most probable underlying facies using a spatial clustering algorithm. In seismic characterization,
this algorithm can yield relevant geological models for subsequent history-matching studies. In history-matching procedures,
it provides informative facies maps as well as starting points for further studies. 相似文献
16.
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
17.
M. Panzeri E. L. Della Rossa L. Dovera M. Riva A. Guadagnini 《Computational Geosciences》2016,20(3):637-653
We present a methodology that allows conditioning the spatial distribution of geological and petrophysical properties of reservoir model realizations on available production data. The approach is fully consistent with modern concepts depicting natural reservoirs as composite media where the distribution of both lithological units (or facies) and associated attributes are modeled as stochastic processes of space. We represent the uncertain spatial distribution of the facies through a Markov mesh (MM) model, which allows describing complex and detailed facies geometries in a rigorous Bayesian framework. The latter is then embedded within a history matching workflow based on an iterative form of the ensemble Kalman filter (EnKF). We test the proposed methodology by way of a synthetic study characterized by the presence of two distinct facies. We analyze the accuracy and computational efficiency of our algorithm and its ability with respect to the standard EnKF to properly estimate model parameters and assess future reservoir production. We show the feasibility of integrating MM in a data assimilation scheme. Our methodology is conducive to a set of updated model realizations characterized by a realistic spatial distribution of facies and their log permeabilities. Model realizations updated through our proposed algorithm correctly capture the production dynamics. 相似文献
18.
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
19.
Antoine Bertoncello Tao Sun Hongmei Li Gregoire Mariethoz Jef Caers 《Mathematical Geosciences》2013,45(7):873-893
Geostatistical simulation methods aim to represent spatial uncertainty through realizations that reflect a certain geological concept by means of a spatial continuity model. Most common spatial continuity models are either variogram, training image, or Boolean based. In this paper, a more recent spatial model of geological continuity is developed, termed the event, or surface-based model, which is specifically applicable to modeling cases with complex stratigraphy, such as in sedimentary systems. These methods rely on a rule-based stacking of events, which are mathematically represented by two-dimensional thickness variations over the domain, where positive thickness is associated with deposition and negative thickness with erosion. Although it has been demonstrated that the surface-based models accurately represent the geological variation present in complex layered systems, they are more difficult to constrain to hard and soft data as is typically required of practical geostatistical techniques. In this paper, we develop a practical methodology for constraining such models to hard data from wells and thickness data interpreted from geophysics, such as seismic data. Our iterative methodology relies on a decomposition of the parameter optimization problem into smaller, manageable problems that are solved sequentially. We demonstrate this method on a real case study of a turbidite sedimentary basin. 相似文献
20.
Diana Dell’Arciprete Chiara Vassena Fulvia Baratelli Mauro Giudici Riccardo Bersezio Fabrizio Felletti 《Hydrogeology Journal》2014,22(4):761-778
In porous aquifers, groundwater flow and solute transport strongly depend on the sedimentary facies distribution at fine scale, which determines the heterogeneity of the conductivity field; in particular, connected permeable sediments could form preferential flow paths. Therefore, properly defined statistics, e.g. total and intrinsic facies connectivity, should be correlated with transport features. In order to improve the assessment of the relevance of this relationship, some tests are conducted on two ensembles of equiprobable realizations, obtained with two different geostatistical simulation methods—sequential indicator simulation and multiple point simulation (MPS)—from the same dataset, which refers to an aquifer analogue of sediments deposited in a fluvial point-bar/channel association. The ensembles show different features; simulations with MPS are more structured and characterised by preferential flow paths. This is confirmed by the analysis of transport connectivities and by the interpretation of data from numerical experiments of conservative solute transport with single and dual domain models. The use of two ensembles permits (1) previous results obtained for single realizations to be consolidated on a more firm statistical basis and (2) the application of principal component analysis to assess which quantities are statistically the most relevant for the relationship between connectivity indicators and flow and transport properties. 相似文献