Applied Geostatistics with SGeMS: A User's Guide

By Nicolas Rémy, Alexandre Boucher, Jianbing Wu. 2009

768 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3019a79feb3a87c88273b2140a7f7518fe5b3612

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Citations in the corpus

A New Differentiable Parameterization Based on Principal Component Analysis for the Low-Dimensional Representation of Complex Geological Models (2014, methodology): “For the Gaussian case, we generated Nr = 200 unconditioned realizations using the sequential Gaussian simulation algorithm within SGeMS (Remy et al. 2009), with a specified set of variogram parameters.”
U-FNO - an enhanced Fourier neural operator based-deep learning model for multiphase flow (2021, methodology): “• kx: The Stanford Geostatistical Modeling Software (SGeMS) [34] is used to generate the heterogeneous kx maps.”
Constraint reduction procedures for reduced‐order subsurface flow models based on POD–TPWL (2015, methodology): “The permeability field, shown in Figure 12, was generated geostatistically using sequential Gaussian simulation within the SGeMS geological modeling package [49].”
A New Data-Space Inversion Procedure for Efficient Uncertainty Quantification in Subsurface Flow Problems (2017, methodology): “We generate an ensemble of Nr = 100 prior models that are conditioned to hard data using the sequential Gaussian simulation algorithm within the SGeMS (Remy et al. 2009) geostatistical toolbox.”
Multiple-point geostatistical modeling based on the cross-correlation functions (2012, methodology): “Strebelle [68] proposed an alternative method—single normal equation simulation (SNESIM)—by using a search tree structure that is an efficient method of accessing highdimensional data that can overcome the problem associated with the original MPS method [49, 59].”
Stochastic Simulation of Patterns Using Distance-Based Pattern Modeling (2010, methodology): “The filtersim results are obtained by using the public domain SGeMS software (Remy et al., 2009).”
A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems (2019, methodology): “ng from the input permeability eld to the dynamic output states (pressure and saturation). Geomodel realizations for this training step can be generated by a geological modeling package such as SGeMS [38]. SGeMS realizations are constructed to honor the features (i.e., multipoint spatial statistics)”
Bayesian inverse problem and optimization with iterative spatial resampling (2010, methodology): “Conditioning models to points data (localized measurements of the variable of interest) is addressed very efficiently by most geostatistical simulation algorithms [Deutsch and Journel, 1992; Remy et al., 2009].”
High-order Stochastic Simulation of Complex Spatially Distributed Natural Phenomena (2010, methodology): “…on the so-called multiple-point simulation algorithms, such as the snesim (Strebelle 2002), filtersim (Zhang et al. 2006; Wu et al. 2008), and simpat (Arpat and Caers 2007) algorithms, and related extensions (Boucher 2009; Chugunova and Hu 2008; Mirowski et al. 2008; Remy et al. 2009; and others).”