Geostatistical reservoir modelling using statistical pattern recognition

被引:92
作者
Caers, J [1 ]
机构
[1] Stanford Univ, Dept Petr Engn, Stanford, CA 94305 USA
关键词
geostatistics; neural networks; inverse and forward modelling; multiple-point statistics;
D O I
10.1016/S0920-4105(01)00088-2
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The traditional practice of geostatistics for reservoir characterization is limited by the variogram which, as a measure of geological continuity, can capture only two-point statistics. Important curvi-linear geological information, beyond the modelling capabilities of the variogram, can be taken from training images and later used in model construction. Training images can provide multiple-point statistics which describe the statistical relation between multiple spatial locations considered jointly. Stochastic reservoir simulation then consists of anchoring the borrowed gee-structures in the form of multiple-point statistics to the actual subsurface hard and soft data. (C) 2001 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:177 / 188
页数:12
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