3D multiple-point statistics simulation using 2D training images

Comunian, Alessandro; Renard, Philippe; Straubhaar, Julien

doi:10.1016/j.cageo.2011.07.009

Cited by 129 publications

(116 citation statements)

References 30 publications

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“…However, a fundamental challenge for the use of MPS for 3-D hydrogeological modelling lies in the difficulty in producing realistic 3-D TIs (e.g. Pérez et al, 2014;Ronayne et al, 2008), and most of the studies in the literature are therefore based on 2-D or quasi-3-D TIs (Jha et al, 2014;Comunian et al, 2012;Feyen and Caers, 2006). Much research has demonstrated the potential of the MPS to simulate 2-D patterns Y.…”

mentioning

confidence: 99%

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Høyer

Vignoli

Hansen

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km 2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90 km 2 . We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modelling.

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mentioning

confidence: 99%

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Høyer

Vignoli

Hansen

et al. 2017

Hydrol. Earth Syst. Sci.

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“…These "copy and paste" methods are simple but certain assumptions need to be invoked. Other methods include complicated statistical simulations such as Comunian et al (2011) and Comunian et al (2012). They used probability aggregation approaches to retrieve statistical information from 2-D TIs which subsequently were applied to simulate a 3-D TI.…”

Section: L He Et Al: Multiple-point Geostatistics In Groundwatermentioning

confidence: 99%

The effect of training image and secondary data integration with multiple-point geostatistics in groundwater modelling

Sonnenborg

Jørgensen

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. Multiple-point geostatistical simulation (MPS)has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from a training image (TI). However, its application in three-dimensional (3-D) simulations has been constrained by the difficulty of constructing a 3-D TI. The object-based unconditional simulation program TiGenerator may be a useful tool in this regard; yet the applicability of such parametric training images has not been documented in detail. Another issue in MPS is the integration of multiple geophysical data. The proper way to retrieve and incorporate information from high-resolution geophysical data is still under discussion. In this study, MPS simulation was applied to different scenarios regarding the TI and soft conditioning. By comparing their output from simulations of groundwater flow and probabilistic capture zone, TI from both sources (directly converted from high-resolution geophysical data and generated by TiGenerator) yields comparable results, even for the probabilistic capture zones, which are highly sensitive to the geological architecture. This study also suggests that soft conditioning in MPS is a convenient and efficient way of integrating secondary data such as 3-D airborne electromagnetic data (SkyTEM), but over-conditioning has to be avoided.

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“…AMag data provided a continuous spatial input to the MPS code that does not rely solely on sparse borehole data to constrain the output. Additionally, the existing geological models provided a conceptual understanding, which was found to present an efficient input for MPS algorithms (Blouin et al 2013;Renard 2007) This study is not bound by complex 3D training image problems as detailed in existing literature (Blouin et al 2013;Comunian et al 2011Comunian et al , 2012, as it has been estimated through magnetic modelling at two field sites (Burns et al 2010;Wilson 2011;Comte et al 2012) that the dykes are sub-vertical cuboids, emplaced within the sandstone. Therefore, the spatial distribution in the Z direction was not computed and data were treated as horizontal two-dimensional (2D).…”

Section: Mps Data Preparation: Training Images and Simulation Gridmentioning

confidence: 99%

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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3D multiple-point statistics simulation using 2D training images

Cited by 129 publications

References 30 publications

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

The effect of training image and secondary data integration with multiple-point geostatistics in groundwater modelling

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

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