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