Bayesian fluid prediction by decoupling both pore structure parameter and porosity

Carbonate reservoirs exhibit complex pore structure, which significantly affects the elastic properties and seismic response, as well prediction of physical parameters. As one main factors impacting fluid prediction, structure parameter directly involves in few inversion methods. In order to predict inversion, a novel quantitative reflection coefficient formula is proposed, that integrate Russell's poroelasticity theory with Sun's petrophysical model. This separates bulk modulus from porosity parameter, allowing for accurate determination pore-fluid distribution through Bayesian framework. Both theoretical model analysis multi-component digital core experiments carbonates validate importance on identification. The practical application carbonate Sichuan Basin demonstrates proposed factor, eliminating illusion caused by heterogeneity within strata, provides more precise reliable predictions compared Russell factor. Furthermore, similarity between factor obtained approximation calculated indirectly method confirms stability accuracy new formula.