Application of multiple attributes fusion technology in the Su-14 Well Block
Wang Xing-Jian1,2, Hu Guang-Min1, and Cao Jun-Xing2
1. School of Communication and Information Engineering, UESTC, Chengdu 610054, China.
2. College of Information Engineering, Chengdu University of Technology, Chengdu 610059, China.
Abstract：
In this study area the geological conditions are complicated and the effective sandstone is very heterogeneous. The sandstones are thin and lateral and vertical variations are large. We introduce multi-attribute fusion technology based on pre-stack seismic data, pre-stack P- and S-wave inversion results, and post-stack attributes. This method not only can keep the fluid information contained in pre-stack seismic data but also make use of the high SNR characteristics of post-stack data. First, we use a one-step recursive method to get the optimal attribute combination from a number of attributes. Second, we use a probabilistic neural network method to train the nonlinear relationship between log curves and seismic attributes and then use the trained samples to find the natural gamma ray distribution in the Su-14 well block and improve the resolution of seismic data. Finally, we predict the effective reservoir distribution in the Su-14 well block.
WANG Xing-Jian,HU Guang-Min,CAO Jun-Xing. Application of multiple attributes fusion technology in the Su-14 Well Block[J]. APPLIED GEOPHYSICS, 2010, 7(3): 257-264.
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2010, Vol. 7 
