Abstract:
The boundary identification and quantitative thickness prediction of channel sand bodies are always difficult in seismic exploration. We present a new method for boundary identification and quantitative thickness prediction of channel sand bodies based on seismic peak attributes in the frequency domain. Using seismic forward modeling of a typical thin channel sand body, a new seismic attribute - the ratio of peak frequency to amplitude was constructed. Theoretical study demonstrated that seismic peak frequency is sensitive to the thickness of the channel sand bodies, while the amplitude attribute is sensitive to the strata lithology. The ratio of the two attributes can highlight the boundaries of the channel sand body. Moreover, the thickness of the thin channel sand bodies can be determined using the relationship between seismic peak frequency and thin layer thickness. Practical applications have demonstrated that the seismic peak frequency attribute can depict the horizontal distribution characteristics of channels very well. The ratio of peak frequency to amplitude attribute can improve the identification ability of channel sand body boundaries. Quantitative prediction and boundary identification of channel sand bodies with seismic peak attributes in the frequency domain are feasible.
SUN Lu-Ping,ZHENG Xiao-Dong,LI Jin-Song et al. Quantitative prediction and application of channel sand bodies based on seismic peak attributes in the frequency domain[J]. APPLIED GEOPHYSICS, 2010, 6(1): 10-17.
[1]
Alamsyah, M. N., Marmosuwito, S., Sutjiningsih, W., and Marpaung, L. P., 2008, Seismic reservoir characterization of Indonesia’s Southwest Betara Field: The Leading Edge, 27(12), 1598 - 1607.
[2]
Chung, H. M., and Lawton, D. C., 1995, Amplitude responses of thin beds: Sinusoidal approximation versus Ricker approximation: Geophysics, 60(3), 223 - 230.
[3]
Cao, Q. R., Li, P., Sun, K. and Li, N., 2007, Using seismic attributes to identify channel sand body: Lithologic Reservoirs, 19(2), 93 - 96.
[4]
Connolly, P., and Kemper, M., 2007, A simple, robust algorithm for seismic net pay estimation: The Leading Edge, 26(10), 1278 - 1282.
[5]
Gridley, J. A., and Partyka, G. A., 1997, Processing and interpretational aspects of spectral decomposition: 67th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1055 - 1058.
[6]
Ji, T. Z., Yang, Y. J., and Li, S. L., 2003, Application of coherence technology in the prediction of channel sand: Geophysical Prospecting for Petroleum, 42(3), 309 - 401.
[7]
Long, J. D., 1995, Neural network BP modeling of the relation between thin bed thickness and amplitude and frequency: Oil Geophysical Prospecting, 30(6), 817 - 822.
[8]
Partyka, G. A., Gridley, J. A., and Lopez, J. A., 1999, Interpretational aspects of spectral decomposition in reservoir characterization: The Leading Edge, 18(3), 353 - 360.
[9]
Ricker, N., 1953, Wavelet contraction, wavelet expansion, and the control of seismic resolution: Geophysics, 18(4), 769 - 792.
[10]
Sun, L. P., Zheng, X. D., Li, J. S., and Shou, H., 2009, Thin-bed thickness calculation formula and its approximation using peak frequency: Applied Geophysics, 6(3), 234 - 240.
[11]
Wang, Z. J., and Huang, J. B., 2006, Identification of micro-fault and sand body by using coherence technique and 3-D visualization: Oil Geophysical Prospecting, 36(3), 378 - 381.
[12]
Wang, J., Chen, Y. L., and Guo, B. X., 2005, 3-D visualization interpretation technique of channels: Oil Geophysical Prospecting, 40(6), 677 - 681.
[13]
Widess, M. B., 1973, How thin is a thin bed?: Geophysics, 38, 1176 - 1180.
[14]
Yao, F. C., and Gan, L. D., 2000, Application and restriction of seismic inversion: Petroleum Exploration and Development, 27(2), 53 - 56.
[15]
Ye, T. R., Su, J. Y., and Liu, X. Y., 2008, Application of seismic frequency division interpretation technology in predicting continental sandstone reservoir in the west of Sichuan province: Geophysical Prospecting for Petroleum, 47(1), 72 - 76.
[16]
Yin, X. Y., Zhang, K., and Zhang, G. Z., 2003, Application of joint time-frequency distribution and its attribution: Oil Geophysical Prospecting, 38(5), 522 - 526.
[17]
Zhao, Z. Z., Zhao, X. Z., and Wang, Y. M., 2005, The theory and application of seismic reservoir prediction: Science Press, Beijing.
[18]
Zhang, M. Z., Yin, X. Y., Yang, C. C., Tan, M. Y., and Song, Y. F., 2007, 3D seismic description for meander sediment micro-facies: Petroleum Geophysics, 5(1), 39 - 42.
[19]
Zhuang, D. H., and Xiao, C. Y., 1996, Thin-bed thickness estimation using neural network: Oil Geophysical Prospecting, 31(3), 394 - 399.