A DIRECT ESTIMATION METHOD FOR THE RUSSELL FLUID FACTOR BASED ON STOCHASTIC SEISMIC INVERSION

In this paper we propose Russell fluid factor direct estimation method based on stochastic seismic inversion. It is a Monte Carlo based strategy for non-linear inversion, which can effectively integrate the high-frequency information of well-logging data and have a higher resolution. And the method is formulated in a Bayesian framework. Firstly, we can calculate the Russell fluid factor using well-logging data and get the a priori information of fluid factor through sequential Gaussian simulation (SGS). Then we construct the likelihood function. Finally, we apply Metropolis algorithm in order to obtain an exhaustive description of the posteriori probability density. In this paper, we use the sequential Gaussian simulation (SGS) in a new implementation way, which can improve the computation speed. According to the numerical calculations, we can conclude that the final results match the model and real well-logging data well and have a higher resolution. In addition, Russell fluid factor we inverted is a sensitive indicator for reservoir fluid identification.