Geobodies Stochastic Analysis for Geological Model Parameter Inference

Chautru, Jean-Marc; Meunier, Renaud; Binet, Hélène; Bourges, Matthieu

doi:10.3997/2214-4609.201413643

Cited by 2 publications

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“…The lithotype is a representation of the spatial relationship between facies (Doligez et al 2011) and can be used to determine the connectivity and relative proportions they exhibit. The method is most commonly applied in attaining field-scale representations of ore deposits (Betzhold and Roth 2000;Yunsel and Ersoy 2011;Renard and Beucher 2012;Talebi et al 2013;Maleki and Emery 2014;Mery et al 2017) and petroleum reservoirs (Chautru et al 2015;Beucher and Renard 2016;Zagayevskiy and Deutsch 2016;Martinius et al 2017;Madani et al 2018), but has also been used at much smaller scales such as in the generation of the micro-structure of solid oxide cells (SOCs). Abdallah et al (2016) applied the truncated plurigaussian model in a set of 3D simulations of three-phase anode layers.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Finite Element Method Integrating a Plurigaussian Random Field Generator for Multi-scale Modelling of Solute Transport in Concrete

et al. 2023

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A new model for the multi-scale simulation of solute transport in concrete is presented. The model employs plurigaussian simulations to generate stochastic representations of concrete micro- and meso-structures. These are idealised as two-phase medium comprising mortar matrix and pores for the micro-structure, and mortar and large aggregate particles for the meso-structure. The generated micro- and meso-structures are employed in a finite element analysis for the simulation of steady-state diffusion of solutes. The results of the simulations are used to calculate effective diffusion coefficients of the two-phase micro- and meso-structures, and in turn, the effective diffusion coefficient at the macro-scale at which the concrete material is considered homogenous. Multiple micro- and meso-structures are generated to account for uncertainty at the macro-scale. In addition, the level of uncertainty in the calculated effective diffusion coefficients is quantified through a statistical analysis. The numerical predictions are validated against experimental observations concerning the diffusion of chloride through a concrete specimen, suggesting that the generated structures are representative of the pore-space and coarse aggregate seen at the micro- and meso-scales, respectively. The method also has a clear advantage over many other structural generation methods, such as packing algorithms, due to its low computational expense. The stochastic generation method has the ability to represent many complex phenomena in particulate materials, the characteristics of which may be controlled through the careful choice of intrinsic field parameters and lithotype rules.

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Section: Introductionmentioning

confidence: 99%

A Statistical Finite Element Method Integrating a Plurigaussian Random Field Generator for Multi-scale Modelling of Solute Transport in Concrete

et al. 2023

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Stochastic Periodic Microstructures for Multiscale Modelling of Heterogeneous Materials

Ricketts

2024

Transp Porous Med

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Plurigaussian simulation is a method of discrete random field generation that can be used to generate many complex geometries depicting real world structures. Whilst it is commonly applied at larger scales to represent geological phenomena, the highly flexible approach is suitable for generating structures at all scales. Here, an extension of plurigaussian simulation to periodic plurigaussian simulation (P-PGS) is presented, such that the resulting fields are periodic in nature. By using periodic Gaussian random fields as components of the method, periodicity is enforced in the generated structures. To substantiate the use of P-PGS in capturing complex heterogeneities in a physically meaningful way, the pore-scale microstructure of cement paste was represented such that its effective properties can be calculated through a computational homogenisation approach. The finite element method is employed to model the diffusion of heat through the medium under dry and saturated pore conditions, where numerical homogenisation is conducted to calculate the effective thermal conductivity of the medium. Comparison of the calculated values with experimental observations indicated that the generated microstructures are suitable for pore-scale representation, given their close match. A maximal error of 1.38% was observed in relation to the numerically determined effective thermal conductivity of mortar paste with air filled pores, and 0.41% when considering water filled pores. As the assumption of a periodic domain is often an underlying feature of numerical homogenisation, this extension of plurigaussian simulation enables a path for its integration into such computational schemes. Article Highlights Integrating P-PGS into numerical homogenisation frameworks enhances complex heterogeneous material representation The flexibility of P-PGS enables a wide range of material microstructures to be represented accurately Use of the generated structures allows material properties to be estimated accurately through numerical homogenisation

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Geobodies Stochastic Analysis for Geological Model Parameter Inference

Cited by 2 publications

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A Statistical Finite Element Method Integrating a Plurigaussian Random Field Generator for Multi-scale Modelling of Solute Transport in Concrete

A Statistical Finite Element Method Integrating a Plurigaussian Random Field Generator for Multi-scale Modelling of Solute Transport in Concrete

Stochastic Periodic Microstructures for Multiscale Modelling of Heterogeneous Materials

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