Abstract The fluid factor plays a pivotal role in pore fluid discrimination and delineation of the hydrocarbon reservoir spatial distribution. However, the conventional construction of fluid factors frequently overlooks pore types, matrix properties, and porosity parameters, potentially introducing additional uncertainties during fluid identification. In contrast, the fluid bulk modulus depends solely on the pore fluid composition and remains independent of the porosity, making it a more robust parameter. Despite this theoretical advantage, conventional inversion based on approximate formulas suffers from inaccuracies owing to simplifying assumptions, such as small incident angles and low contrast. Therefore, we propose a novel inversion methodology that utilizes the precise Zoeppritz equation to directly estimate the fluid bulk modulus. Employing a Bayesian approach, we formulated an objective function targeting the simultaneous estimation of fluid bulk modulus, porosity, shear modulus, and density, solved via the Iteratively Reweighted Least Squares (IRLS) approach. Crucially, the results demonstrate that this non-linear strategy effectively overcomes the intrinsic limitations of linearized approximations in high-contrast media. By directly estimating the fluid bulk modulus, the method exhibits superior sensitivity to hydrocarbon saturation compared to conventional elastic parameters. This improvement significantly enhances the reliability of reservoir delineation in complex geologic settings, providing a robust technical foundation for reducing exploration uncertainty.
Zhang et al. (Wed,) studied this question.