A library of training images for fluvial and deepwater reservoirs and associated code

Geostatistical algorithms that consider multiple-point statistics are becoming increasingly popular. These methods allow for the reproduction of complicated features beyond the commonly implemented two-point statistic: the covariance or semivariogram. In practice, it is not possible to infer many multiple-point statistics directly from the available data; therefore, it is common to borrow statistics from training images. Training images are exhaustive gridded numerical models that have spatial features deemed relevant to the site being characterized.