Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics

By Sébastien Strebelle. 2002

1,532 citations recorded by the source corpus.

Subfields: Environmental Engineering, Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/8b8fc61bc0968332a122cf4c3ae0a8ee8b296198

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In Environmental Engineering

In Mechanical Engineering

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Citations in the corpus

Inverse methods in hydrogeology: Evolution and recent trends (2014, background): “A possible722 approach is with multiple point geostatistics (Guardiano and Srivastava, 1993; Strebelle, 2002).”
Efficient real-time reservoir management using adjoint-based optimal control and model updating (2006, methodology): “Further, it is assumed that we have some prior knowledge of the reservoir, which informs us that the reservoir is a fluvial channelized reservoir as depicted by the training image [35] shown in figure 3, with the channel sand permeability being about 10 Darcy and the background sand permeability abo”
A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterisation of well catchments (2009, methodology): “Examples are the Boolean method [60], the sequential indicator simulation method [51], the truncated plurigaussian method [88], the method of transition probabilities [25] and Multiple-Point (MP) geostatistics [58,73,120].”
Conditional Simulation with Patterns (2007, methodology): “ Strebelle (2002) proposes a more ad-hoc approach based on an original idea of Guardiano and Srivastava (1993).”
The Probability Perturbation Method: A New Look at Bayesian Inverse Modeling (2006, methodology): “The multiple-point algorithm, “snesim” of Strebelle (2002) is used to simulate the facies model.”
An efficient maximum entropy approach for categorical variable prediction (2011, methodology): “Sequential simulation using multi-points statistics (Strebelle, 2002; Mariethoz & Renard, 2010) allows us to go beyond the twopoint statistical models inherent to all variogram-based methods and provides very convincing images.”
The Direct Sampling method to perform multiple‐point geostatistical simulations (2010, methodology): “For existing MP methods, several techniques can be found in the literature to account for nonstationarity either of the TI or of the simulated field [Chugunova andHu, 2008;De Vries et al., 2009; Journel, 2002; Strebelle, 2002].”
Stochastic Simulation of Patterns Using Distance-Based Pattern Modeling (2010, background): “Another approach that does not suffer from this issue uses probability fields to control the occurrence of attributes (Strebelle, 2002; Harding et al., 2005).”
Blocking Moving Window algorithm: Conditioning multiple‐point simulations to hydrogeological data (2010, methodology): “…indicator simulation method [Gomez‐Hernandez and Srivastava, 1990], the truncated pluri‐Gaussian method [Le Loc’h and Galli, 1997], the transition probabilities method [Carle and Fogg, 1997] and multiple‐point (MP hereinafter) geostatistics [Guardiano and Srivastava, 1993; Strebelle, 2002].”
Simulation of rainfall time series from different climatic regions using the direct sampling technique (2014, background): “Introduced by Guardiano15 and Srivastava (1993) and widely developed during the last decade (Strebelle, 2002; Allard et al., 2006; Zhang et al., 2006; Arpat and Caers, 2007; Honarkhah and Caers, 2010; Straubhaar et al., 2011; Tahmasebi et al., 2012), MPS is a family of geostatistical techniques…”
Connectivity metrics for subsurface flow and transport (2013, methodology): “[80] used the probability perturbation inverse method [81] and multiple-point statistics [40] to analyze on the same site the anomalous pressure signals observed in six observation wells during a pumping test.”
Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information (2012, methodology): “It originally was not until an efficient way of storing multiple-point statistics in machine memory was proposed that the use of multiple-point based models became computationally feasible (Strebelle, 2002).”