Application of Multiple Point Geostatistics to Non-stationary Images

Vries, Luis Manuel de; Carrera, Jesús; Falivene, O.; Gratacós, Òscar; Slooten, Luit Jan

doi:10.1007/s11004-008-9188-y

Cited by 71 publications

(36 citation statements)

References 24 publications

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“…In theory, MPS implementations assume stationary TIs (Y. and MPS application to strongly non-stationary systems is currently an area of research (e.g. Honarkhah and Caers, 2012;Straubhaar et al, 2011;de Vries et al, 2009;Chugunova et al, 2008). The TI used for this study was generated by following the unique criterion that the unconditioned simulation (obtained by using SNESIM through its implementation in SGeMS) could satisfactorily reproduce the expected geological structures.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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 methods can better handle complex patterns; however, handling nonstationarity remains a challenge. See [23,24] for recent development in this area.…”

Section: Parametrization Methods: a Brief Overviewmentioning

confidence: 99%

Effective Parametrization for Reliable Reservoir Performance Predictions

Kalla

2012

Int. J. UncertaintyQuantification

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“…The simulation of elongated connected structures in particular is a recurrent problem. Although it received a lot of attention recently de Vries et al, 2009;Honarkhah and Caers, 2012), treatment of some types of nonstationarity remains to be investigated. Another issue is that although some quantitative validation methods have been proposed (Boisvert et al, 2010;de Iaco and Maggio, 2011;Jung and Aigner, 2012), one of the most reliable validation criteria is still visually qualitative: i.e.…”

Section: A Brief Historical Perspectivementioning

confidence: 99%

Bridges between multiple-point geostatistics and texture synthesis: Review and guidelines for future research

Mariethoz

Lefèbvre

2014

Computers & Geosciences

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a b s t r a c tMultiple-Point Simulations (MPS) is a family of geostatistical tools that has received a lot of attention in recent years for the characterization of spatial phenomena in geosciences. It relies on the definition of training images to represent a given type of spatial variability, or texture. We show that the algorithmic tools used are similar in many ways to techniques developed in computer graphics, where there is a need to generate large amounts of realistic textures for applications such as video games and animated movies. Similarly to MPS, these texture synthesis methods use training images, or exemplars, to generate realistic-looking graphical textures.Both domains of multiple-point geostatistics and example-based texture synthesis present similarities in their historic development and share similar concepts. These disciplines have however remained separated, and as a result significant algorithmic innovations in each discipline have not been universally adopted. Texture synthesis algorithms present drastically increased computational efficiency, patterns reproduction and user control. At the same time, MPS developed ways to condition models to spatial data and to produce 3D stochastic realizations, which have not been thoroughly investigated in the field of texture synthesis.In this paper we review the possible links between these disciplines and show the potential and limitations of using concepts and approaches from texture synthesis in MPS. We also provide guidelines on how recent developments could benefit both fields of research, and what challenges remain open.

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Application of Multiple Point Geostatistics to Non-stationary Images

Cited by 71 publications

References 24 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

Effective Parametrization for Reliable Reservoir Performance Predictions

Bridges between multiple-point geostatistics and texture synthesis: Review and guidelines for future research

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