To integrate the multiple variables, traditional methodologies suggest modelling the cross variograms and co-simulation using the Linear Model of Coregionalization (LMC), that is difficult task. This framework combines two approaches: Bayesian Updating and Sequential Gaussian Simulation considering all information regarding the relationship of the multiple variables without modelling the LMC. Firstly, at each soft data location, hard and soft data were incorporated using Bayesian Updating, which considers multivariate correlations between variables, to build a conditional distribution. Secondly, n-possible values are drawn from these conditional distributions and imputed as hard data. Next, Sequential Gaussian Simulation was performed to complete all the grid nodes using the original and previously simulated hard data. The simulated models were compared with the models obtained by Sequential Gaussian Simulation (SGS) using only the original hard data. The results show the soft data addition improve the accuracy of the models once an appropriate methodology is used to incorporate them.