• 责任者：Zhang Tuanfeng (Geology and Rock Physics Department ; Schlumberger Doll Research ; One Hampshire Street ; Cambridge ; MA ; 02139 ; USA)
• 刊名：Earth Science Frontiers
• 出版年：2008
• 卷期：15(1)
• 关键词：地质统计学 ; geostatistics ; 储集层描述 ; reservoir description
• 页码：p.26-35
• 图表：7 illus., 32 refs.
• 分类号：1003
• issnNo：ISSN1005-2321
• isbnNo：CN11-3370/P

The newly introduced multiple-point geostatistics provides a powerful tool for geologists and reservoir modelers to bring such prior models, both explicitly and quantitatively, into reservoir modeling using training images. A training image is the numerical representation of a prior geological model that contains the patterns believed to exist in realistic reservoirs under study. By reproducing high order statistics, multiple-point algorithm can capture curvilinear structures or complex features from the training image and anchor them to specific well locations. This article presents the principle of multiple-point geostatistics and its applications to building 3-D reservoir models with the stress on the importance of training image concept.