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- W2765745576 abstract "The development of effective algorithms for the simulation of facies in petroleum systems continues to be limited to techniques relying on linear spatial dependence; Truncated Gaussian Simulation, Plurigaussian Simulation and Sequential Indicator Simulation, in addition to pattern-based techniques based on Multiple Point Statistics. Recent research in the development of new geostatistical techniques for continuous variables has found that the requirement of linear spatial dependence is very rarely satisfied. Datasets from micrometre to kilometre scale have been analysed spatially and, in all cases, significant non-linearity in the spatial dependence structure have been observed. This paper replaces the Gaussian ‘engine’ in Truncated Gaussian Simulation and Plurigaussian Simulation, which requires linear spatial dependence, with a copula-based algorithm that is able to model non-linear dependence structures. The advantage is a facies model that does not contain the symmetric spatial artefacts that characterise Gaussian-based models. In flow simulation terms, this means that the connectivity (and directional connectivity) of different facies within the reservoir architecture to be modelled with greater fidelity, honouring the expected continuities of each of the facies elements. Furthermore, extending recent research by Horning and Bardossy on directional and order asymmetry for continuous variables to consider their application to facies models allows the development of facies-models that are able to represent the directional asymmetry found in sedimentary depositional systems. This allows a better representation of the distal and proximal elements in depositional systems as well as the opportunity to model complex asymmetries associated with meandering channel and estuaries." @default.
- W2765745576 created "2017-11-10" @default.
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- W2765745576 date "2017-01-01" @default.
- W2765745576 modified "2023-09-24" @default.
- W2765745576 title "A New Method for Facies Simulation Using Non-Linear Geostatistics with Directional and Order Asymmetry" @default.
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- W2765745576 doi "https://doi.org/10.2118/186367-ms" @default.
- W2765745576 hasPublicationYear "2017" @default.
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