Geostatistical Reservoir Modeling

By Clayton V. Deutsch. 2002

1,018 citations recorded by the source corpus.

Subfields: Geochemistry and Petrology, Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/ddb331e4b3db30ea87eb3f1888ea13a1d489d285

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In Geochemistry and Petrology

In Mechanical Engineering

In Ocean Engineering

Citations in the corpus

Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM+ data over a boreal forest. (2005, methodology): “Geostatistics is concerned with a variety of techniques aimed at understanding and modeling spatial variability through prediction and simulation (Deutsch, 2002; Journel, 1989; Journel & Huijbregts, 1978; Goovaerts, 1997).”
Characterizing geotechnical anisotropic spatial variations using random field theory (2013, background): “Jaksa and Fenton (2000) and Deutsch (2002) provided an excellent summary of common variogram models used in practice.”
Statistical approach to inverse distance interpolation (2009, methodology): “Among statistical methods, geostatistical kriging-based techniques, including Simple and Ordinary Kriging, Universal Kriging and Simple Cokriging (see Journel, 1986; Cressie, 1993; Deutsch, 2002) have been often used for spatial analysis.”
Reconstruction of three-dimensional porous media using generative adversarial neural networks (2017, neutral): “Spatial probabilistic models such as truncated Gaussian processes or sequential indicator simulation have been widely applied in the geosciences to model the spatial distribution of materials [16].”
Non-stationary Geostatistical Modeling Based on Distance Weighted Statistics and Distributions (2012, methodology): “Suitable spatial declustering techniques include cell declustering (Isaaks and Srivastava 1989; Deutsch and Journel 1998), polygonal declustering (Isaaks and Srivastava 1989; Deutsch 2002), and weights obtained from the cross-validation simple kriging variance (Bourgault 1997).”
Evaluation of Gaussian approximations for data assimilation in reservoir models (2012, methodology): “Geostatistical techniques can then be used to generate realizations of reservoir parameters conditioned to static data [11].”