The conventional methods of two-point statistics (TPS) applied in geological simulation have preserved histograms and variograms. However, they have serious limitations in creating complex reliable distributions of geological bodies. Multiplepoint statistics (MPS) methods enable solving a wider spectrum of geological simulation problems while also preserving the common statistical parameters. However, the application of these methods in practice is complicated by the requirement of reliable litho-facial models and the corresponding training images. A possible solution could be the development of a library of training images (TI) to cover the variety of sedimentation environments and a wide distribution of statistical parameters in geo models. The selection of training images and their scales for specific simulation tasks is based on matching statistical parameters; the simulation result in this case inherits these parameters. This approach ensures the match in general statistical parameters of the models created using TPS and MPS. However, the difference in higher-order distributions may affect flow model properties.
In this paper, we investigate some issues of implementing MPS methods, developing a training image library, as well as selecting and scaling TIs. Some comparison results between TPS and MPS models are presented.