A multiple-point geostatistical method based on geological vector information

  • Wenjie Feng
  • Shenghe WuEmail author
Original Paper


The reproduction of the non-stationary distribution and detailed characteristics of geological bodies is the main difficulty of reservoir modeling. Recently developed multiple-point geostatistics can represent a stationary geological body more effectively than traditional methods. When restricted to a stationary hypothesis, multiple-point geostatistical methods cannot simulate a non-stationary geological body effectively, especially when using non-stationary training images (TIs). According to geologic principles, the non-stationary distribution of geological bodies is controlled by a sedimentary model. Therefore, in this paper, we propose auxiliary variables based on the sedimentary model, namely geological vector information (GVI). GVI can characterize the non-stationary distribution of TIs and simulation domains before sequential simulation, and the precision of data event statistics will be enhanced by the sequential simulation’s data event search area limitations under the guidance of GVI. Consequently, the reproduction of non-stationary geological bodies will be improved. The key features of this method are as follows: (1) obtain TIs and geological vector information for simulated areas restricted by sedimentary models; (2) truncate TIs into a number of sub-TIs using a set of cut-off values such that each sub-TI is stationary and the adjacent sub-TIs have a certain similarity; (3) truncate the simulation domain into a number of sub-regions with the same cut-off values used in TI truncation, so that each sub-region corresponds to a number of sub-TIs; (4) use an improved method to scan the TI or TIs and construct a single search tree to restore replicates of data events located in different sub-TIs; and (5) use an improved conditional probability distribution function to perform sequential simulation. A FORTRAN program is implemented based on the SNESIM.


Multiple-point geostatistics Non-stationary Training image Geological vector information 



This study is supported by both of the National Planned Major Science and Technology Projects of China (No. 2011ZX05009-003) and the National Natural Science Foundation of China (No. 41372116). We thank our colleagues, Senlin Yin, Zhen Li, Mei Huang, and Yafei Jing for their help on the data analysis and program debugging.


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Copyright information

© Saudi Society for Geosciences 2016

Authors and Affiliations

  1. 1.College of GeosciencesChina University of Petroleum (Beijing)BeijingChina
  2. 2.State Key Laboratory for Petroleum Resources and ProspectingChina University of Petroleum (Beijing)BeijingChina

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