Considering the impact of viscosity estimation on the fluid model, our goal is to propose an approach for tuning some parameters that can improve the outcomes of a CSP based method. Besides that, we also demonstrate a scheme for performing sensitivity analysis and provide uncertainties information for the tuned parameters. In particular, we are interested in a even more difficult problem, which is the estimation of heavy oils viscosities. Due little information about the heavier fractions during the compositional characterization procedure, this kind of mixture has intrinsic associated uncertainties. More specifically, we provide a reliable method for tuning the CSP model proposed by Lindeloff et al., which is an adaptation of the Pedersen et al. method to this kind of fluids. To calibrate the model, a Bayesian approach is employed using a Transitional Markov Chain Monte Carlo method called Cascading Adaptive Transition Metropolis in Parallel 8 (CAT-MIP), which results in probability distributions for tuning parameter as output, in contrast with classical fitting methods based on deterministic continuous optimization problems (e.g., Least-Squares regressions), where only parameter values are known after the calibration procedure. The probability distributions provide information about the effect of uncertainties incorporated from data on the model predictive capabilities 9,10 . Moreover, the model outcomes are given as credible regions, which can shed light on conditions where predictions are poor or inappropriate. Since that the Bayesian approach is computationally intensive, we use an enhanced Elementary Effects method 11 to evaluate overall sensitivity measures for CSP tuning parameters and determine the most influ-ential parameters to be tuned, resulting in an efficient Bayesian calibration using Sensitivity Analysis information. Such Bayesian calibration approach, as far as the authors are aware, is unreported in the CSP viscosity model literature.