Multiple-Point Statistics Simulation Models: Pretty Pictures or Decision-Making Tools?

Strebelle, Sebastien

doi:10.1007/s11004-020-09908-8

Cited by 16 publications

(11 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2b, [7]). The inability of the SNESIM MPS method to honour the connectivity of the training image is seldom acknowledged but is a recognised restriction of the method [3,[8][9][10].…”

Section: Connectivity In Facies Models and Natural Systemsmentioning

confidence: 99%

Compression-Based Modelling Honouring Facies Connectivity in Diverse Geological Systems

Manzocchi

Walsh

López-Cabrera

et al. 2023

Springer Proceedings in Earth and Environmental Sciences

View full text Add to dashboard Cite

In object- or pixel-based modelling, facies connectivity is tied to facies proportion as an inevitable consequence of the modelling process. However, natural geological systems (and rule-based models) have a wider range of connectivity behaviour and therefore are ill-served by simple modelling methods in which connectivity is an unconstrained output property rather than a user-defined input property. The compression-based modelling method decouples facies proportions from facies connectivity in the modelling process and allows models to be generated in which both are defined independently. The two-step method exploits the link between the connectivity and net:gross ratio of the conventional (pixel- or object-based) method applied. In Step 1 a model with the correct connectivity but incorrect facies proportions is generated. Step 2 applies a geometrical transform which scales the model to the correct facies proportions while maintaining the connectivity of the original model. The method is described and illustrated using examples representative of a poorly connected deep-water depositional system and a well-connected fluid-driven vein system.

show abstract

“…2b, [7]). The inability of the SNESIM MPS method to honour the connectivity of the training image is seldom acknowledged but is a recognised restriction of the method [3,[8][9][10].…”

Section: Connectivity In Facies Models and Natural Systemsmentioning

confidence: 99%

Compression-Based Modelling Honouring Facies Connectivity in Diverse Geological Systems

Manzocchi

Walsh

López-Cabrera

et al. 2023

Springer Proceedings in Earth and Environmental Sciences

View full text Add to dashboard Cite

show abstract

“…Different from previously stated pixel-based implementations that simulate each point separately, pattern-based methods simulate a set of points at a time . Pattern-based implementations perform better in reproducing the pattern features in TI; however, the data conditioning is limited . In recent years, some new applications based on computer vision and deep learning techniques (DCGAN, LGCNN, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…34 Pattern-based implementations perform better in reproducing the pattern features in TI; however, the data conditioning is limited. 35 In recent years, some new applications based on computer vision and deep learning techniques (DCGAN, 36 LGCNN, 37 etc.) have been proposed.…”

Section: ■ Introductionmentioning

confidence: 99%

Multiple-Point Geostatistics Simulation of the Natural Gas Hydrate Distribution at the Pore Scale

Li²,

Chen

et al. 2023

Energy Fuels

View full text Add to dashboard Cite

Heat and mass transfer in a natural gas hydrate reservoir is strongly affected by the hydrate pore habit. Some present simplified models used in numerical simulation of a hydrate-bearing sediment are difficult to catch features of the hydrate distribution in sediments precisely. Using an improved multiple-point geostatistics simulation method, this study generates a series of images of porous media that reproduce the hydrate distribution information on a training image. The improved algorithm completely solves the problem that the hydrate blocks the boundary and makes the variogram and visual properties similar to the training image. Some typical phenomena of hydrate formation appear in the results, such as Ostwald ripening and the shift of hydrate pore habit from grain coating toward pore filling. The feasibility of predicting the pore habit of a hydrate with time using this novel method is proven by the verification case. This study offers a new approach to predict the distribution behavior of a hydrate, and this algorithm may be applied widely by scanning training images from different reservoirs.

show abstract

“…Second-order spatial simulation methods (Journel and Huijbregts 1978 ; David 1988 ; Goovaerts 1997 ; Chiles and Delfiner 2012 ) have been used to quantify spatial uncertainty. Since the early 1990s, a new multiple-point statistics (MPS) spatial simulation framework has been developed to overcome the limitations of previous approaches (Guardiano and Srivastava 1993 ; Gómez-Hernández and Srivastava 2021 ; Strebelle 2002 , 2021 ; Journel 2005 ; Liu et al 2006 ; Arpat and Caers 2007 ; Hu and Chugunova 2008 ; de Vries et al, 2008 ; Mariethoz et al 2010 ; Honarkhah and Caers 2010 ; De Iaco and Maggio 2011 ; Stien and Kolbjørnsen 2011 ; Chatterjee et al 2012 ; Lochbühler et al 2013 ; Rezaee et al 2013 ; Toftaker and Tjelmeland 2013 ; Mustapha et al 2014 ; Strebelle and Cavelius 2014 ; Zhang et al 2017 ). MPS methods are able to capture and produce more complex spatial patterns than second-order spatial simulation methods by using multiple-point spatial templates that replace the well-established two-point spatial statistics.…”

Section: Introductionmentioning

confidence: 99%

A New Non-stationary High-order Spatial Sequential Simulation Method

et al. 2022

View full text Add to dashboard Cite

A new non-stationary, high-order sequential simulation method is presented herein, aiming to accommodate complex curvilinear patterns when modelling non-Gaussian, spatially distributed and variant attributes of natural phenomena. The proposed approach employs spatial templates, training images and a set of sample data. At each step of a multi-grid approach, a template consisting of several data points and a simulation node located in the center of the grid is selected. To account for the non-stationarity exhibited in the samples, the data events decided by the conditioning data are utilized to calibrate the importance of the related replicates. Sliding the template over the training image generates a set of training patterns, and for each pattern a weight is calculated. The weight value of each training pattern is determined by a similarity measure defined herein, which is calculated between the data event of the training pattern and that of the simulation pattern. This results in a non-stationary spatial distribution of the weight values for the training patterns. The proposed new similarity measure is constructed from the high-order statistics of data events from the available data set, when compared to their corresponding training patterns. In addition, this new high-order statistics measure allows for the effective detection of similar patterns in different orientations, as these high-order statistics conform to the commutativity property. The proposed method is robust against the addition of more training images due to its non-stationary aspect; it only uses replicates from the pattern database with the most similar local high-order statistics to simulate each node. Examples demonstrate the key aspects of the method.

show abstract

Multiple-Point Statistics Simulation Models: Pretty Pictures or Decision-Making Tools?

Cited by 16 publications

References 19 publications

Compression-Based Modelling Honouring Facies Connectivity in Diverse Geological Systems

Compression-Based Modelling Honouring Facies Connectivity in Diverse Geological Systems

Multiple-Point Geostatistics Simulation of the Natural Gas Hydrate Distribution at the Pore Scale

A New Non-stationary High-order Spatial Sequential Simulation Method

Contact Info

Product

Resources

About