Multi-dimensional pre-stack seismic inversion based on sparse representation

被引：0

作者：

Yang S. ^{[1
]}

Wu G. ^{[1
,2
]}

Zhang M. ^{[3
]}

Du Z. ^{[1
]}

Shan J. ^{[1
]}

Liang Z. ^{[1
]}

机构：

[1] School of Geosciences, China University of Petroleum(East China), Qingdao, 266580, Shandong

[2] Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266580, Shandong

[3] Shengli Geophysical Research Institute of SINO-PEC, Dongying, 257022, Shandong

来源：

Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2020年 / 55卷 / 02期

关键词：

FAVO; Interference suppression; Multi-dimension; Pre-stack inversion; Reservoir prediction; Sparse representation;

D O I：

10.13810/j.cnki.issn.1000-7210.2020.02.019

中图分类号：

学科分类号：

摘要：

It is difficult to characterize thin reservoirs through conventional pre-stack and post-stack inversion. Interference effects generated by stacking may fade, distort, and suppress effective signals; thus, we suggest avoiding stacking to preserve signals. Pre-stack attributes vary with offset and frequency; this means that FAVO inversion based on high-resolution spectral decomposition may be capable of addressing the issue of fluid detection. We present multi-dimensional pre-stack seismic inversion based on sparse representation, and the point is no stacking. The first step is to extract angle gathers at the zone of interest and then perform high-resolution time-frequency decomposition for each single angle gather extracted in terms of sparse representation. The second step is to establish the mapping relationship between impedance and seismic data in accordance with the Bayesian theory and add some perturbation, calculated using non-linear optimization, to the initial model. The last step is to take the result at the previous frequency as the constraint to perform inversion at the next frequency until the final output at each angle is obtained. The case study in Prospect A shows that constrained inversion at each angle and frequency generates multi-dimensional results with high precision; this facilitates thin reservoir characterization. © 2020, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.

引用

页码：398 / 410

页数：12

共 25 条

[1] Chapman M., Maultzsch S., Liu E., Et al., The effect of fluid saturation in an anisotropic multi-scale equant porosity model, Journal of Applied Geophysics, 54, 3-4, pp. 191-202, (2003)
[2] Chapman M., Liu E., Li X.Y., The influence of abnormally high reservoir attenuation on the AVO signature, The Leading Edge, 24, 11, pp. 1120-1125, (2005)
[3] Chapman M., Liu E., Li X.Y., The influence of fluid-sensitive dispersion and attenuation on AVO analysis, Geophysical Journal of the Royal Astronomical Society, 167, 1, pp. 89-105, (2006)
[4] Wilson A., Chapman M., Li X.Y., Frequency-dependent AVO inversion, SEG Technical Program Expanded, 28, pp. 341-345, (2009)
[5] Wu X.Y., Chapman M., Li X., Estimating seismic dispersion from prestack data using frequency-dependent AVO analysis, Journal of Seismic Exploration, 23, 3, pp. 425-429, (2014)
[6] Zhang S., Yin X., Zhang G., Et al., Inversion method for the velocity dispersion-dependent attribute of P-wave, Geophysical Prospecting for Petroleum, 50, 3, pp. 219-224, (2011)
[7] Wu G., Tian J., AVO inversion based on scale constraints, Oil Geophysical Prospecting, 49, 6, pp. 1157-1164, (2014)
[8] Yang Q., Wu G., Multi-scale seismic inversion method based on Bayesian theory, Progress in Geophysics, 31, 3, pp. 1246-1256, (2016)
[9] Wu J., Wu G., A method of 1D mesoscopic digital rock physics in the frequency domain, Oil Geophysical Prospecting, 53, 1, pp. 105-112, (2018)
[10] Li K., Yin X., Zong Z., Time-frequency-domain FAVO fluid discrimination method based on matching pursuit spectrum decomposition, Acta Petrolei Sinica, 37, 6, pp. 777-786, (2016)

← 1 2 3 →