Fluid identification and effective fracture prediction based on frequency-dependent AVOAz inversion for fractured reservoirs

Fluid and effective fracture identification in reservoirs is a crucial part of reservoir prediction. The frequency-dependent AVO inversion algorithms have proven to be effective for identifying fluid through its dispersion property. However, the conventional frequency-dependent AVO inversion algorithms based on Smith & Gidlow and Aki & Richards approximations do not consider the acquisition azimuth of seismic data and neglect the effect of seismic anisotropic dispersion in the actual medium. The aligned fractures in the subsurface medium induce anisotropy. The seismic anisotropy should be considered while accounting for the seismic dispersion properties through fluid-saturated fractured reservoirs. Anisotropy in such reservoirs is frequency-related due to wave-induced fluid-flow (WIFF) between interconnected fractures and pores. It can be used to identify fluid and effective fractures (fluid -saturated) by using azimuthal seismic data via anisotropic dispersion properties. In this paper, based on Ruger's equation, we derived an analytical expression in the frequency domain for the frequency dependent AVOAz inversion in terms of fracture orientation, dispersion gradient of isotropic background rock, anisotropic dispersion gradient, and the dispersion at a normal incident angle. The frequency-dependent AVOAz equation utilizes azimuthal seismic data and considers the effect of both isotropic and anisotropic dispersion. Reassigned Gabor Transform (RGT) is used to achieve high resolution frequency division data. We then propose the frequency-dependent AVOAz inversion method to identify fluid and characterize effective fractures in fractured porous reservoirs. Through application to high-qualified seismic data of dolomite and carbonate reservoirs, the results show that the method is useful for identifying fluid and effective fractures in fluid-saturated fractured rocks. (c) 2021 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).