: Asphaltene precipitation and deposition have always been a major issue in the oil industry. Asphaltene deposition at all stages of production and oil processing can cause problems such as well clogging, formation damage and damage to process equipment. For this reason, achieving a model to predict the onset conditions and the amount of asphaltene precipitation has always been desirable in the oil industry. So far, various models have been proposed to predict asphaltene precipitation. One of the most popular and widely used methods is using equations of state. Despite their simplicity and high popularity, cubic equations of states have not been able to meet expectations in predicting asphaltene deposition. One of the recent advances in thermodynamic modeling of asphaltene precipitation is the use of statistical EoS including the PC-SAFT EoS. In previous studies, the PC-SAFT EoS has shown good accuracy in predicting the phase behavior of asphaltene precipitation. In previous researches, the used live oils were among the light oils with low asphaltene content. In this research, an attempt has been made to predict the onset conditions of asphaltene precipitation in Iranian heavy live oils with high asphaltene content using statistical EoS and then validate with experimental data. For this purpose, thermodynamic modeling of asphaltene precipitation in four live oils using PC-SAFT has been investigated. Three of these oils are heavy oils with high asphaltene content. First, the oils were characterized by two different methods. In the first method, the adjustable parameters of the thermodynamic model, which are the aromaticity of aromatics + resins and the aromaticity of asphaltene, were adjusted separately. But in the second method, both parameters of the model were tuned simultaneously. Then the amounts of precipitated asphaltene were modeled using both characterization methods and the results were compared to experimental data. Finally, asphaltene precipitation was simulated using PVTsim software and the results were compared to the results of the model. In two of used oils, the model was able to predict the trend and amount of experimental data and AADs were 14% and 26%. In the other two oils, the model was able to predict the trend of experimental data, but was not successful in predicting the amount of them and showed a large deviation. One of the most important reasons this high error could be an experimental error in measuring the amount of precipitated asphaltene. In order to overcome this problem, a new weight factor was added to the model and experimental error was considered in thermodynamic modeling. By adding this new factor, in one of the oils the error was reduced by about 70% and reached to 24%. In other oil, the error decreased by 90% to 31%. Also, the results of simulation using PVTsim software presented more error than the used model. Keywords: Thermodynamic modeling , Asphaltene precipitation, PC-SAFT EoS, Characterization, Aromaticity