Fractional S-transform?part 2: Application to reservoir prediction and fluid identification
Du Zheng-Cong1,2, Xu De-Ping1,3, and Zhang Jin-Ming4
1. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu 610059, China.
2. Panzhihua University, Panzhihua 617000, China.
3. Geomathematics Key Laboratory of Sichuan Province of Chengdu University of Technology, Chengdu 610059, China.
4. Geophysical college of Chengdu University of Technology, Chengdu 610059, China.
Abstract：
The fractional S-transform (FRST) has good time–frequency focusing ability. The FRST can identify geological features by rotating the fractional Fourier transform frequency (FRFTfr) axis. Different seismic signals have different optimal fractional parameters which is not conducive to multichannel seismic data processing. Thus, we first decompose the common-frequency sections by the FRST and then we analyze the low-frequency shadow. Second, the combination of the FRST and blind-source separation is used to obtain the independent spectra of the various geological features. The seismic data interpretation improves without requiring to estimating the optimal fractional parameters. The top and bottom of a limestone reservoir can be clearly recognized on the common-frequency section, thus enhancing the vertical resolution of the analysis of the low-frequency shadows compared with traditional ST. Simulations suggest that the proposed method separates the independent frequency information in the time–fractional-frequency domain. We used field seismic and well data to verify the proposed method.
. Fractional S-transform?part 2: Application to reservoir prediction and fluid identification[J]. APPLIED GEOPHYSICS, 2016, 13(2): 343-352.
[1]
Akan, A., and Çekiç, Y., 2003, A fractional Gabor expansion: Journal of the Franklin Institute, 340(5), 391-397.
[2]
Almeida, L. B., 1994, The fractional Fourier transform and time-frequency representations: IEEE Tran Signal Processing, 42(11), 3084-3091.
[3]
Bingham, E. and Hyvärinen, A., 2000, A fast fixed-point algorithm for independent component analysis of complex-valued signals: International Journal of Neural Systems, 10(1), 1-8.
[4]
Chen, X. H., He, Z. H., Huang, D. J., et al., 2009, Low frequency shadow detection of gas reservoir s in time-frequency domain: Chinese J. Geophysics. (in Chinese), 52(1), 215-221.
[5]
Comen, P., 1994, Independent component analysis, a new concept?: Signal Processing, 36(3), 287-314.
[6]
Durak, L., and Arikan, O., 2003, Short-time Fourier transform: two fundamental properties and optimal implementation: IEEE Trans. on Signal Processing, 51(5), 1231-1242.
[7]
He, Z. H., Xiong, X. J., and Bian, L., 2008, Numerical simulation of seismic low-frequency shadows and its application: Applied Geophysics, 5(4), 301-306.
[8]
Huang, Y., and Suter, B., 1998, The Fractional Wave Packet Transform: Multidimensional Systems and Signal Processing, 9(4), 399-402.
[9]
Hyvärinen, A., 1999, Fast and robust fixed-point algorithm for independent component analysis. IEEE Trans. on Neural Network, 10(3), 626-634.
[10]
Li, Y., and Zheng, X., 2007, Wigner-Ville distribution and its application in seismic attenuation estimation: Applied Geophysics, 4(4), 245-254.
[11]
Rioul, O., and Vetterli, M., 1991, Wavelets and signal processing: IEEE Signal Processing, 8(4), 14-38.
[12]
Stockwell, R. G., Mansinha, L., and Lowe, R. P., 1996, Localization of the complex spectrum: the S transform: IEEE trans. on signal processing, 44(4), 998-1001.
[13]
Tao, R., Li, Y. L., and Wang, Y., 2010, Short-Time Fractional Fourier Transform and Its Applications: IEEE Transactions on Signal Processing, 58(5), 2568-2580.
[14]
Wexler, R., 1990, Discrete Gabor expansions: Signal Processing, 21(3), 207-220.
[15]
Xu, D. P., and Guo, K., 2012, Fractional S transform−part 1: Theory: Applied Geophysics, 9(1), 73-79.
2016, Vol. 13 
