Reservoir Facies Modelling: New Advances in MPS

Harding, Andrew; Strebelle, Sebastien; Levy, Marjorie; Thorne, Julian A.; Xie, Deyi; Leigh, S. P.; Preece, Rachel; Scamman, Robert

doi:10.1007/978-1-4020-3610-1_57

Cited by 17 publications

(11 citation statements)

References 5 publications

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“…By using a search tree, the TI is scanned once, and all the replicas of the patterns are stored. Therefore, it is possible to apply SNESIM to 3D reservoirs [1,10,32,33,49,69] and hydrogeological modeling [13,25,37,60,61]. However, SNESIM also suffers from intensive memory and CPU requirements, when used in the simulation of large 3D reservoirs.…”

Section: Introductionmentioning

confidence: 98%

Multiple-point geostatistical modeling based on the cross-correlation functions

Tahmasebi¹,

Hezarkhani²,

2012

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An important issue in reservoir modeling is accurate generation of complex structures. The problem is difficult because the connectivity of the flow paths must be preserved. Multiple-point geostatistics is one of the most effective methods that can model the spatial patterns of geological structures, which is based on an informative geological training image that contains the variability, connectivity, and structural properties of a reservoir. Several pixel-and pattern-based methods have been developed in the past. In particular, pattern-based algorithms have become popular due to their ability for honoring the connectivity and geological features of a reservoir. But a shortcoming of such methods is that they require a massive data base, which make them highly memory-and CPU-intensive. In this paper, we propose a novel methodology for which there is no need to construct pattern data base and small data event. A new function for the similarity of the generated pattern and the training image, based on a cross-correlation (CC) function, is proposed that can be used with both categorical and continuous training images. We combine the CC function with an overlap strategy and a new approach, adaptive recursive template splitting along a raster path, in order to develop an algorithm, which we call cross-correlation simulation (CCSIM), for generation of the realizations of a reservoir with accurate conditioning and continuity. The performance of CCSIM is tested for a variety of training images. The results, when compared with those of the previous methods, indicate significant improvement in the CPU and memory requirements.

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Section: Introductionmentioning

confidence: 98%

Multiple-point geostatistical modeling based on the cross-correlation functions

Tahmasebi¹,

Hezarkhani²,

2012

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“…The mp-geostatistics concept was further elaborated by Strebelle [29,30] and has recently been applied successfully in numerous petroleum engineering applications (e.g., [17,19,26,27,32]). Strebelle [29] developed the single normal equation simulation (snesim) algorithm, an efficient non-iterative sequential simulation algorithm that obtains mp statistics from the training image(s) and anchors them to the actual subsurface data.…”

Section: Introductionmentioning

confidence: 99%

Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations

Feyen

Caers

2006

Advances in Water Resources

139

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“…The first example is one in which the TS is a shallow-water, tidal-dominated system [28] with a size 200×500. The comparison of the original TS and its reconstructed model, as well as the computed MPC functions for several rezalizations and the TS are shown in Fig.…”

Section: B Ecological Systemsmentioning

confidence: 99%

Reconstruction of nonstationary disordered materials and media: Watershed transform and cross-correlation function

Tahmasebi

Sahimi

2015

Phys. Rev. E

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Nonstationary disordered materials and media -those for which the probability distribution function of any property varies spatially when shifted in space -are abundant and encountered in astrophysics, oceanography, air pollution patterns, large-scale porous media, biological tissues and organs, and composite materials. Their reconstruction and modeling is a notoriously difficult and largely unsolved problem. We propose a method for reconstructing a broad class

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Reservoir Facies Modelling: New Advances in MPS

Cited by 17 publications

References 5 publications

Multiple-point geostatistical modeling based on the cross-correlation functions

Multiple-point geostatistical modeling based on the cross-correlation functions

Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations

Reconstruction of nonstationary disordered materials and media: Watershed transform and cross-correlation function

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