Improving sequential simulation with a structured path guided by information content

被引:45
作者
Liu, YH [1 ]
Journel, A [1 ]
机构
[1] Stanford Univ, Dept Geol & Environm Sci, Stanford Ctr Reservoir Forecasting, Stanford, CA 94305 USA
来源
MATHEMATICAL GEOLOGY | 2004年 / 36卷 / 08期
关键词
random path; multiple-point geostatistics; post-processing; snesim; long-range continuity;
D O I
10.1023/B:MATG.0000048800.72104.de
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Multiple-point simulation is a newly developed geostatistical method that aims at combining the strengths of two mainstream geostatistical methods: object-based and pixel-based methods. It maintains the flexibility of pixel-based algorithms in data conditioning, while enhancing its capability of reproducing realistic geological shapes, which is traditionally reserved to object-based algorithms. However, the current snesim program for multiple-point simulation has difficulty in reproducing large-scale structures, which have a significant impact on the flow response. To address this problem, we propose to simulate along a structured path based on an information content measure. This structured path accounts for not only the information from the data, but also some prior structural information provided by geological knowledge. Various case studies show a better reproduction of large-scale structures. This concept of simulating along a structured path guided by information content can be applied to any sequential simulation algorithms, including traditional variogram-based two-point geostatistical algorithms.
引用
收藏
页码:945 / 964
页数:20
相关论文
共 11 条
[1]  
DEUTSCH C, 1997, MATH GEOL, V28, P857
[2]  
Deutsch C. V., 1992, GSLIB GEOSTATISTICAL
[3]  
Gilbert R., 2004, LEADING EDGE, V23, P784, DOI DOI 10.1190/1.1786903
[4]  
Gomez-Hernandez J. J., 1991, THESIS STANFORD U ST
[5]  
GUARDIANO FB, 1992, GEOSTATISTICS TROIA, V1, P113
[6]  
LIU Y, 2003, THESIS STANFORD U ST
[7]  
LIU Y, 2004, AAPG B, V88
[8]  
REMY N, 2001, 14 SCRF STANF U
[9]  
Strebelle S., 2000, THESIS STANFORD U ST
[10]  
Strebelle S, 2000, GEOSTATISTICS 2000 C, V1, P381