Seismic Inversion for Petrophysical-properties Based on Bayesian Classification

Petrophysical properties estimation based on geophysical data is an important step in reservoir precise description. In this paper, a new petrophysical parameters inversion method based on Bayesian formulation is proposed. This approach uses statistical rock physics model to link seismic response and petrophysical properties, and extend the available data to generate training data for the classification system based on MCMC method. In a Bayesian formulation, we can construct a classification function or model using Bayesian classification method. Finally, we can not only get the most likely facies and pore fluids, but also evaluate the reliability of the results based on the global maximum of the posterior distribution (MAP). The methodology has been applied to carbonate reservoir from basin in west of China. The results indicate that we can effectively eliminate the influence of the erosion surface and find the real carbonate fractured-vuggy reservoir based on the MAP result of porosity.