Frequency-dependent nonlinear AVO inversion for fluid mobility in viscoelastic media

Abstract

Reservoir fluid mobility is defined as the ratio of rock permeability to fluid viscosity, which is an important attribute to guide the prediction of high-quality oil and gas reservoirs. Frequency-dependent amplitude variation with offset (AVO) inversion, based on viscoelastic theory, is a useful tool for fluid identification. In conventional pre-stack inversion, linear approximations are usually used to calculate the reflection coefficients, while nonlinear inversions are only occasionally carried out. However, the nonlinear equation for the reflection coefficient, derived from the exact Zoeppritz equation, has higher accuracy and fewer assumptions than the linear approximations. Therefore, we derive a nonlinear frequency-dependent reflection coefficient equation of the PP-wave in terms of P-wave velocity reflectivity, S-wave velocity reflectivity and fluid mobility, based on the relationship among fluid mobility, incident angle, and seismic wave frequency. We consider viscoelastic media and frequency-dependent responses to improve the authenticity of the reflection coefficients. Additionally, we develop a split-step inversion method for fluid mobility based on the Artificial Neural Network Inversion (ANNI) algorithm. Synthetic and field data examples demonstrate that the proposed split-step inversion method outperforms traditional inversion methods that rely on approximate equations for predicting underground reservoirs, improving the stability of fluid mobility parameter inversion.