Poroelasticity and Linearized AVO Inversion with Fluid Modulus

Abstract

A fluid discrimination approach incorporating linearized poroelasticity theory and pre-stack seismic reflection inversion with Bayesian inference is proposed to identify the fluid underground. The fluid modulus is defined as the fluid indicator which is mainly affected by the fluid contained in reservoirs. A novel linearized P-wave reflectivity equation coupling the fluid modulus is derived to avoid the complicated nonlinear relationship between the fluid modulus with seismic data. Convoluting this linearized P-wave reflectivity with seismic wavelets as the forward solver, a practical pre-stack Bayesian seismic inversion method is presented to estimate the fluid modulus directly. Cauchy and Gaussian distribution are utilized for prior probability distribution of the model parameters and the likelihood function to enhance the inversion resolution. The preconditioned conjugate gradient method is coupled in the optimization of the inversion objective function to weaken the strong degree of correlation among the four model parameters and enhance the stability of multiple parameters estimation simultaneously. The synthetic test demonstrates the feasibility and stability of the proposed novel seismic coefficient equation and inversion approach. Test on a real data set illustrates the efficiency and success of the proposed approach in differentiating the fluid filled reservoirs.