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.
The National Natural Science Foundation of China and China Petroleum & Chemical Corporation Co-funded Project (Grant Nos 40839905 and 40739907).
Cite this article:
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.
[1]
Barnes, A. E., 1999, Seismic attributes past, present, and future: 69th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 892 - 895.
[2]
Cao, H., 2002, Some cognition on the application of seismic attributes: Progress in Exploration Geophysics, 25(5), 18 - 22.
[3]
Chen, Z. D., 1998, Reservoir seismic attribute optimization method: Petroleum Industry Press.
[4]
Davies, D. K, Vessell, R. K., and Bernal, M. C., 1996, Flow unit modeling in complex reservoirs: Annual Convention, American Association of Petroleum Geologists, 5, pp.33.
[5]
Guo, J. L, and Chen, C., 2006, Application of stochastic simulation methods in predicting effective distribution of sand bodies in the Sulige- 6 Well Block: Journal of Jianghan Petroleum Institute, 28(5), 54 - 57.
[6]
Hampson, D. P., Schuelke, J. S., and Quirein, J. A., 2001, Use of multiattribute transforms to predict log properties from seismic data: Geophysics, 66(1), 220 - 236.
[7]
Neves, F. A., and Triebwasser, H., 2006, Multi-attribute seismic volume facies classification for predicting fractures in carbonate reservoirs: The Leading Edge, vol 25(2), 698 - 700
[8]
Russell, B., Hampson, D., Schuelke, J., and Quiren, J., 1997, Multiattribute seismic analysis: The Leading Edge, 16(10), 1439 - 1444.
[9]
Russell, B., Ross, C. P. and Lines, L., 2002, Neural networks and AVO: The Leading Edge, 21(3), 268 - 277.
[10]
Sukmono, S., and Santoso, D.,2006,Integrating seismic attributes for reservoir characterization in Melandong Field,Indonesia:The Leading Edge,vol 25(2), 532 - 538
[11]
Yi, C. X., 1995, Nonlinear science and its application in geology: Beijing Meteorological Press.
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