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 prestack inversion, linear approximations are usually used to calculate the reflection coefficients, whereas 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. In addition, we develop a split-step inversion method for fluid mobility based on the artificial neural network inversion algorithm. Synthetic and field data examples demonstrate that our 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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call