Multiple-point geostatistical simulation using the bunch-pasting direct sampling method

Rezaee, Hassan; Mariethoz, Grégoire; Koneshloo, Mohammad; Asghari, Omid

doi:10.1016/j.cageo.2013.01.020

Cited by 63 publications

(27 citation statements)

References 22 publications

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“…Application of DS to real data is limited in literature at present (Jha et al 2013;Mariethoz and Renard 2010;Meerschman et al 2014;Pirot et al 2014) and developmental advancements are being published: bunch-pasting DS (Rezaee et al 2013); and fast DS (Abdollahifard and Faez 2014). The work presented here explores the use of…”

Section: Mps Geophysics and Groundwater Modellingmentioning

confidence: 99%

“…MPS has been advanced and tested for several years, e.g. threedimensional (3D) application (Comunian et al 2011Michael et al 2010), direct sampling (Mariethoz et al 2010;Rezaee et al 2013) and geological structure (Huysmans and Dassargues 2009;2011;Rezaee et al 2014), demonstrating the ability of reproducing realistic geological models in a stochastic framework, and allowing a quantification of the uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Integrating aerial geophysical data in multiple-point statistics simulations to assist groundwater flow models

et al. 2015

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The process of accounting for heterogeneity has made significant advances in statistical research, primarily in the framework of stochastic analysis and the development of multiple-point statistics (MPS). Among MPS techniques, the direct sampling (DS) method is tested to determine its ability to delineate heterogeneity from aerial magnetics data in a regional sandstone aquifer intruded by low-permeability volcanic dykes in Northern Ireland, UK. The use of two two-dimensional bivariate training images aids in creating spatial probability distributions of heterogeneities of hydrogeological interest, despite relatively 'noisy' magnetics data (i.e. including hydrogeologically irrelevant urban noise and regional geologic effects). These distributions are incorporated into a hierarchy system where previously published density function and upscaling methods are applied to derive regional distributions of equivalent hydraulic conductivity tensor K. Several K models, as determined by several stochastic realisations of MPS dyke locations, are computed within groundwater flow models and evaluated by comparing modelled heads with field observations. Results show a significant improvement in model calibration when compared to a simplistic homogeneous and isotropic aquifer model that does not account for the dyke occurrence evidenced by airborne magnetic data. The best model is obtained when normal and reverse polarity dykes are computed separately within MPS simulations and when a probability threshold of 0.7 is applied. The presented stochastic approach also provides improvement when compared to a previously published deterministic anisotropic model based on the unprocessed (i.e. noisy) airborne magnetics. This demonstrates the potential of coupling MPS to airborne geophysical data for regional groundwater modelling.

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Section: Mps Geophysics and Groundwater Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrating aerial geophysical data in multiple-point statistics simulations to assist groundwater flow models

et al. 2015

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“…Among the most efficient MPS algorithms are patch‐based methods, whereby instead of simulating a single node from a probability model, entire patterns are pasted into the simulation grid. Several algorithms have been developed to accomplish this [ Chatterjee et al ., ; Honarkhah and Caers , ; Mahmud et al ., ; Rezaee et al ., ; Tahmasebi et al ., ; Tahmasebi et al ., ; Zhang et al ., ]. They offer significant computational gains but tend to have a lower patterns diversity than pixel‐based methods [ Mahmud et al ., ; Tan et al ., ], which is problematic for applications where sweeping a wide model space is important [ Caers , ], such as inverse problems [ Laloy et al ., ; Lochbühler et al ., ; Saibaba and Kitanidis , ].…”

Section: Introductionmentioning

confidence: 99%

Patch‐based iterative conditional geostatistical simulation using graph cuts

Mariethoz

et al. 2016

Water Resources Research

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Training image-based geostatistical methods are increasingly popular in groundwater hydrology even if existing algorithms present limitations that often make real-world applications difficult. These limitations include a computational cost that can be prohibitive for high-resolution 3-D applications, the presence of visual artifacts in the model realizations, and a low variability between model realizations due to the limited pool of patterns available in a finite-size training image. In this paper, we address these issues by proposing an iterative patch-based algorithm which adapts a graph cuts methodology that is widely used in computer graphics. Our adapted graph cuts method optimally cuts patches of pixel values borrowed from the training image and assembles them successively, each time accounting for the information of previously stitched patches. The initial simulation result might display artifacts, which are identified as regions of high cost. These artifacts are reduced by iteratively placing new patches in high-cost regions. In contrast to most patch-based algorithms, the proposed scheme can also efficiently address point conditioning. An advantage of the method is that the cut process results in the creation of new patterns that are not present in the training image, thereby increasing pattern variability. To quantify this effect, a new measure of variability is developed, the merging index, quantifies the pattern variability in the realizations with respect to the training image. A series of sensitivity analyses demonstrates the stability of the proposed graph cuts approach, which produces satisfying simulations for a wide range of parameters values. Applications to 2-D and 3-D cases are compared to state-of-the-art multiple-point methods. The results show that the proposed approach obtains significant speedups and increases variability between realizations. Connectivity functions applied to 2-D models transport simulations in 3-D models are used to demonstrate that pattern continuity is preserved.

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“…Design and development of new methods is necessary. Among these methods, direct sampling simulation is an approach used to process the non-stationary reservoir simulation [10][11][12]. Instead of scanning the entire training image, the direct sampling method only searches a reasonable data event within a certain area.…”

Section: Introductionmentioning

confidence: 99%

“…There are some improved methods, such as the geometric transformation-based method and the partitionbased simulation methods [8][9][10]. Some non-stationary characteristics in a fan delta reservoir have been obtained.…”

Section: Introductionmentioning

confidence: 99%

A location-based multiple point statistics method: modelling the reservoir with non-stationary characteristics

Yin¹,

Feng²

2017

Open Geosciences

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Abstract:In this paper, a location-based multiple point statistics method is developed to model a non-stationary reservoir. The proposed method characterizes the relationship between the sedimentary pattern and the deposit location using the relative central position distance function, which alleviates the requirement that the training image and the simulated grids have the same dimension. The weights in every direction of the distance function can be changed to characterize the reservoir heterogeneity in various directions. The local integral replacements of data events, structured random path, distance tolerance and multi-grid strategy are applied to reproduce the sedimentary patterns and obtain a more realistic result. This method is compared with the traditional Snesim method using a synthesized 3-D training image of Poyang Lake and a reservoir model of Shengli Oilfield in China. The results indicate that the new method can reproduce the non-stationary characteristics better than the traditional method and is more suitable for simulation of delta-front deposits. These results show that the new method is a powerful tool for modelling a reservoir with non-stationary characteristics.

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Multiple-point geostatistical simulation using the bunch-pasting direct sampling method

Cited by 63 publications

References 22 publications

Integrating aerial geophysical data in multiple-point statistics simulations to assist groundwater flow models

Integrating aerial geophysical data in multiple-point statistics simulations to assist groundwater flow models

Patch‐based iterative conditional geostatistical simulation using graph cuts

A location-based multiple point statistics method: modelling the reservoir with non-stationary characteristics

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