Application of Multiple-Point Geostatistics on Modelling Groundwater Flow and Transport in a Cross-Bedded Aquifer

Huysmans, Marijke; Dassargues, Alain

doi:10.1007/978-90-481-2322-3_13

Cited by 16 publications

(20 citation statements)

References 12 publications

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“…The methods paBor and s2Dcd are tested using two training images that represent some typical sedimentary structures mapped in the Brussels Sands by Huysmans and Dassargues (2010). In the training images, one can distinguish a matrix of clay-rich bottom sets and distinct mud drapes (Fig.…”

Section: Reproduction Of Sedimentary Structuresmentioning

confidence: 99%

“…Finding a 3D TI is often challenging. This is claimed, for example, in the work of Huysmans and Dassargues (2010), where two 2D training images are deduced from two quarry walls in the Brussels Sands formation (Belgium), or in the work of Le Coz et al (in press), where only one horizontal training image is available from detailed aerial/satellite images of the Komadougou Yobé alluvium (Niger).…”

Section: Introductionmentioning

confidence: 99%

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

Comunian¹,

Renard²,

Straubhaar³

2012

Computers & Geosciences

129

101

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a b s t r a c tOne of the main issues in the application of multiple-point statistics (MPS) to the simulation of threedimensional (3D) blocks is the lack of a suitable 3D training image. In this work, we compare three methods of overcoming this issue using information coming from bidimensional (2D) training images. One approach is based on the aggregation of probabilities. The other approaches are novel. One relies on merging the lists obtained using the impala algorithm from diverse 2D training images, creating a list of compatible data events that is then used for the MPS simulation. The other (s2Dcd) is based on sequential simulations of 2D slices constrained by the conditioning data computed at the previous simulation steps. These three methods are tested on the reproduction of two 3D images that are used as references, and on a real case study where two training images of sedimentary structures are considered. The tests show that it is possible to obtain 3D MPS simulations with at least two 2D training images. The simulations obtained, in particular those obtained with the s2Dcd method, are close to the references, according to a number of comparison criteria. The CPU time required to simulate with the method s2Dcd is from two to four orders of magnitude smaller than the one required by a MPS simulation performed using a 3D training image, while the results obtained are comparable. This computational efficiency and the possibility of using MPS for 3D simulation without the need for a 3D training image facilitates the inclusion of MPS in Monte Carlo, uncertainty evaluation, and stochastic inverse problems frameworks.

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Section: Reproduction Of Sedimentary Structuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Comunian¹,

Renard²,

Straubhaar³

2012

Computers & Geosciences

129

101

View full text Add to dashboard Cite

show abstract

“…Vertical connectivity may be enhanced by bottomsets thinning and wedging out (e.g., Fig. 14), especially if the bottomset is clay rich (e.g., Huysmans et al 2008;Huysmans and Dassargues 2010;Possemiers et al 2012;Reesink et al 2014). Conversely, a sand-dominated bottomset with periodic mud drapes (e.g., Fig.…”

Section: Bottomsets In Reservoir Sandstonesmentioning

confidence: 99%

Bottomset Architecture Formed In the Troughs of Dunes and Unit Bars

Herbert

Alexander

2018

Journal of Sedimentary Research

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Bottomsets are formed in the troughs of migrating bedforms. The flow and sediment dynamics downstream of bedforms differ, and consequently, the thickness, grain size, and internal structure of bottomsets vary greatly. A classification of the controls on bottomset formation is proposed related to two-and three-dimensional processes, flow and sediment unsteadiness, and pre-existing deposits. Structures and deposits created by flow and sediment unsteadiness as well as pre-existing deposits are also considered in relation to changes they can induce in subsequent trough processes. The classification is illustrated by observations from laboratory flumes, the modern Burdekin River (Australia), and the rock record (The Roaches Grit, England, and Hawkesbury Sandstone, Australia).In bottomset examples described in this paper, internal variability, both vertically and laterally, was much greater than that within the dominant foreset component. Complex compound structures were created by the interaction and predominance of different bottomset controls. This, combined with their high preservation potential, makes them a highly useful paleoenvironmental indicator. Bottomset variation (e.g., changes in the abundance of mud laterally) will influence the permeability heterogeneity of fluvial reservoir rocks. Bottomsets formed under relatively steady conditions are likely to be laterally extensive and have similar characteristics over their entire length. They may act as significant barriers to vertical flow. Conversely, bottomsets generated in unsteady regimes tend to be highly variable, potentially creating conduits between cross-bed sets aiding inter-set permeability.

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“…As noted by Huysmans and Dassargues (2009), applications of multiple-point geostatistics to real-world cases in hydrogeology are scarce; one objective of using data from Yucca Flat is to demonstrate the use of multiple-point geostatistics in a context with potential practical hydrogeologic applications. The data from Yucca Flat also present a common challenge for 3D geologic modeling, where vertical (1D) subsurface data must be combined with horizontal (2D) surface data to develop a 3D model of the subsurface geology.…”

Section: Purposementioning

confidence: 99%

“…Multiple-point geostatistics offers a solution to this problem by imitating complex spatial patterns by using a training image (Caers and Zhang, 2004;Hu and Chugunova, 2008). Examples of applications of multiple-point geostatistics in hydrogeology include Feyen and Caers (2006), Ronayne and others (2008), and Huysmans and Dassargues (2009).…”

Section: Introductionmentioning

confidence: 99%