nerc.ac.uk

An improved method for the modeling of frequency-dependent amplitude-versus-offset variations

Guo, Zhiqi; Liu, Cai; Li, Xiangyang; Lan, Huitian. 2015 An improved method for the modeling of frequency-dependent amplitude-versus-offset variations. IEEE Geoscience and Remote Sensing Letters, 12 (1). 63-67. 10.1109/LGRS.2014.2326157

Full text not available from this repository. (Request a copy)

Abstract/Summary

A proper description of the frequency-dependent seismic amplitude variation versus offset (AVO) responses should consider the effect of both the layered structure of a reservoir and the dispersive and attenuated property of the media in the reservoir. We propose an improved method to seamlessly link the rock physics modeling and the calculation for frequency-dependent reflection coefficients based on propagator matrix method. The improved AVO modeling method is implemented in frequency-wavenumber domain, and can accurately considers dispersion and attenuation that described by complex and frequency-dependent elastic properties predicted by rock physic models. Therefore, the improved method avoids errors resulting from truncating imaginary parts of the elastic properties as adopted by the conventional Zoeppritz-equation-based method. Moreover, the proposed method considers the intrinsic contribution of the layered structure to frequency-dependence of AVO responses, which has been ignored by current conventional methods. In addition, the method provides an efficient way to calculate seismograms for a dispersive and attenuated layered model. Finally, modeling results show the applicability of the improved method for the interpretation of complex frequency-dependent abnormalities, and indicate the potential for fluid detection in a layered reservoir.

Item Type: Publication - Article
Digital Object Identifier (DOI): 10.1109/LGRS.2014.2326157
ISSN: 1545-598X
Date made live: 30 Sep 2014 13:03 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/508539

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...