Connectivity in Pixel-Based Facies Models

Walsh, Deirdre A.; Manzocchi, Tom

doi:10.1007/s11004-021-09931-3

Cited by 5 publications

(16 citation statements)

References 35 publications

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“…Pixel-based models (PBM) built using the sequential indicator (SIS), or truncated Gaussian (TGS) methods have similar thresholds as OBMs, with NTG C ≤ 28%, as do models built using the pixel-based SNESIM multiple point (MPS) method even if the training images used to create the models have much lower connectivity (Fig. 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%

“…3d). The compression-based geometrical transformation can be applied to pixel-based as well as object-based models, implying that it can be used to create facies models which are both conditioned to well data, and constrained by user-defined facies connectivity [3,7,10].…”

Section: Compression-based Facies Modellingmentioning

confidence: 99%

“…Compression-based facies modelling is a two-step process (Fig. 4, [1,7]). In Step 1, a conventional object-or pixel-based model is created with a net:gross value equal to the target AR value (Fig.…”

Section: Compression-based Facies Modellingmentioning

confidence: 99%

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Compression-Based Modelling Honouring Facies Connectivity in Diverse Geological Systems

Manzocchi

Walsh

López-Cabrera

et al. 2023

Springer Proceedings in Earth and Environmental Sciences

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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.

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Section: Connectivity In Facies Models and Natural Systemsmentioning

confidence: 99%

Section: Compression-based Facies Modellingmentioning

confidence: 99%

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Compression-Based Modelling Honouring Facies Connectivity in Diverse Geological Systems

Manzocchi

Walsh

López-Cabrera

et al. 2023

Springer Proceedings in Earth and Environmental Sciences

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“…Percolation thresholds of models generated using pixel-based methods have not received as much attention as of object-based methods, but a recent systematic study (Walsh and Manzocchi 2021a ) established the three-dimensional thresholds of representative isotropic models built using an industrial implementation of the SIS, TGS and SNESIM MPS methods with different variograms (or training images). The study concluded that these pixel-based models have NTG 0.28, with the simplest algorithms having NTG 0.13.…”

Section: Connectivity In Facies Models and Natural Depositional Systemsmentioning

confidence: 99%

“…The objectives of the current paper are to illustrate the CBM method using: (1) different underlying geostatistical methods (TGS as well as OBM); (2) different measures of connectivity (a percolation threshold-based criterion as well as AR); (3) different conceptual geological models (low connectivity sedimentary deposits, high connectivity veins) and (4) conditioning to well data. Section 2 discusses connectivity in models and natural systems and, following Walsh and Manzocchi ( 2021a ), describes the relationship between AR and the proximity of the system to its percolation threshold (parameter P , after Sahimi 1995 ; Renard and De Marsily 1997 ). Section 3 uses CBM to model a hierarchical OBM of a poorly connected deep water lobe system constrained by AR, while Sect.…”

Section: Introductionmentioning

confidence: 99%

Compression-based Facies Modelling

et al. 2023

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Simple object- or pixel-based facies models use facies proportions as the constraining input parameter to be honored in the output model. The resultant interconnectivity of the facies bodies is an unconstrained output property of the modelling, and if the objects being modelled are geometrically representative in three dimensions, commonly-available methods will produce well-connected facies when the model net:gross ratio exceeds about 30%. Geological processes have more degrees of freedom, and facies in high net:gross natural systems often have much lower connectivity than can be achieved by object-based or common implementations of pixel-based forward modelling. The compression method decouples facies proportion from facies connectivity in the modelling process and allows systems to be generated in which both are defined independently at input. The two-step method first generates a model with the correct connectivity but incorrect facies proportions using a conventional method, and then applies a geometrical transform to scale the model to the correct facies proportions while retaining the connectivity of the original model. The method, and underlying parameters, are described and illustrated using examples representative of low and high connectivity geological systems.

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The Suitability of Different Training Images for Producing Low Connectivity, High Net:Gross Pixel-Based MPS Models

Walsh

López-Cabrera

Manzocchi

2023

Springer Proceedings in Earth and Environmental Sciences

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Pixel-based multiple-point statistical (MPS) modelling is an appealing geostatistical modelling technique as it easily honours well data and allows use of geologically-derived training images to reproduce the desired heterogeneity. A variety of different training image types are often proposed for use in MPS modelling, including object-based, surface-based and process-based models. The purpose of the training image is to provide a description of the geological heterogeneities including sand geometries, stacking patterns, facies distributions, depositional architecture and connectivity. It is, however, well known that pixel-based MPS modelling has difficulty reproducing facies connectivity, and this study investigates the performance of a widely-available industrial SNESIM algorithm at reproducing the connectivity in a geometrically-representative, idealized deep-water reservoir sequence, using different gridding strategies and training images. The findings indicate that irrespective of the sand connectivity represented in the training image, the MPS models have a percolation threshold that is the same as the well-established 27% percolation threshold of random object-based models. A more successful approach for generating poorly connected pixel-based MPS models at high net:gross ratios has been identified. In this workflow, a geometrical transformation is applied to the training image prior to modelling, and the inverse transformation is applied to the resultant MPS model. The transformation is controlled by a compression factor which defines how non-random the geological system is, in terms of its connectivity.

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Connectivity in Pixel-Based Facies Models

Cited by 5 publications

References 35 publications

Compression-Based Modelling Honouring Facies Connectivity in Diverse Geological Systems

Compression-Based Modelling Honouring Facies Connectivity in Diverse Geological Systems

Compression-based Facies Modelling

The Suitability of Different Training Images for Producing Low Connectivity, High Net:Gross Pixel-Based MPS Models

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