Seismic estimation of the fluid factor and shear modulus plays an important role in reservoir fluid identification and characterization. Various amplitude variation with offset inversion methods have been used to estimate these two parameters, which are generally based on approximate formulations of the Zoeppritz equations. However, the accuracy of these methods is limited because the forward modeling ability of approximate equations is incorrect under the conditions of strong impedance contrast and large incidence angles. Therefore, to improve the estimation accuracy, we have used the Zoeppritz equations to directly invert for the fluid factor and shear modulus. Based on the poroelasticity theory, we derive the Zoeppritz equations in a new form containing the fluid factor, shear modulus, density, and dry-rock velocity ratio squared. The objective function is then constructed using these equations in a Bayesian framework with the addition of a differentiable Laplace distribution blockiness constraint term to the prior model to enhance the fluid boundaries. Finally, the nonlinear objective function is solved by combining the Taylor expansion and the iterative reweighted least-squares algorithm. Numerical experiments indicate that the inversion accuracy of our method may heavily depend on the parameter of the dry-rock velocity ratio square that is assumed to be static. However, tests on synthetic and field data show that our method can estimate the fluid factor and shear modulus with satisfactory accuracy in the case of choosing a reasonable static value of this parameter. In addition, we determine that the accuracy of this method is higher than that of the linearized formulation.

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