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Modeling complex reservoir geometries with multiple-point statistics
Mathematical Geology, 1996Large-scale reservoir architecture constitutes first-order reservoir heterogeneity and dietates to a large extent reservoir flow behavior. It also manifests geometric characteristics beyond the capability of traditional geostatistical models conditioned only on single-point and two-point statistics.
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Multiple-point Statistics Simulations Accounting for Block Data
Proceedings, 2015Multiple-point statistics methods allow to generate highly heterogeneous fields reproducing the spatial features within a given training image. Whereas punctual conditioning data can be handled straightforwardly, dealing with information defined at larger scales is challenging.
J. Straubhaar*, P. Renard, G. Mariethoz
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Handling Soft Probabilities in Multiple Point Statistics Simulation
2013This paper is presenting a methodology to handle rigorously soft probabilities in Multiple Point Statistics (MPS) simulation for facies modeling. It is based on the second generation algorithm for MPS simulation using efficient Direct Sampling of the training image.
Pierre Biver +4 more
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GPU-accelerated Direct Sampling method for multiple-point statistical simulation
Computers & Geosciences, 2013Abstract Geostatistical simulation techniques have become a widely used tool for the modeling of oil and gas reservoirs and the assessment of uncertainty. The Direct Sampling (DS) algorithm is a recent multiple-point statistical simulation technique.
Tao Huang 0011 +3 more
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Accelerating simulation for the multiple-point statistics algorithm using vector quantization
Physical Review E, 2018Multiple-point statistics (MPS) is a prominent algorithm to simulate categorical variables based on a sequential simulation procedure. Assuming training images (TIs) as prior conceptual models, MPS extracts patterns from TIs using a template and records their occurrences in a database.
Chen, Zuo, Zhibin, Pan, Hao, Liang
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Prediction of permeability for porous media reconstructed using multiple-point statistics
Physical Review E, 2004To predict multiphase flow through geologically realistic porous media, it is necessary to have a three-dimensional (3D) representation of the pore space. We use multiple-point statistics based on two-dimensional (2D) thin sections as training images to generate geologically realistic 3D pore-space representations.
Hiroshi, Okabe, Martin J, Blunt
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Integrating Multiple-point Statistics into Sequential Simulation Algorithms
2005Most conventional simulation techniques only account for two-point statistics via the modeling of the variogram of the regionalized variable or of its indicators. These techniques cannot control the reproduction of multiple-point statistics that may be critical for the performance of the models given the goal at hand (flow simulation in petroleum ...
Julian M. Ortiz, Xavier Emery
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Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics
Mathematical Geology, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Practical Implementation Details of Multiple Point Statistical Simulation
2016Best practice and recommendations for multiple point statistics (MPS) simulation are presented. Three main contributions are: (1) assessing the stationarity of training images (TI) and determining the characteristics of TI's that result in realizations that reproduce features found in the TI; (2) determining optimal input parameters of MPS simulation; (
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