Although CO2 injection is one of the most common methods in enhanced oil recovery, it could alter fluid properties of oil and cause some problems such as asphaltene precipitation. The maximum amount of asphaltene precipitation occurs near the fluid pressure and concentration saturation. According to the description of asphaltene deposition onset, the bubble point pressure has a very special importance in optimization of the miscible CO2 injection. The purpose of this research is to predict the onset of asphaltene and bubble point pressure of fluid reservoir using artificial intelligence developed models including a software simulator called “Intelligent Proxy Simulator (IPS)” based on structure artificial neural networks and “adaptive neural fuzzy inference system”, which is a combination of fuzzy logic and neural networks. To evaluate the predictions by artificial intelligence networks at the onset of deposition, a solid model using Winprop software was employed. Standing correlations were used for comparison of bubble point pressure. The results obtained using artificial intelligence models in prediction of the onset of asphaltene deposition and bubble point pressure during injection of CO2 were more accurate than those obtained from the thermodynamics Solid model and the Standing correlation respectively.
 Asghari M., Dong, “Development of a Correlation Between Performance of CO2 Flooding and the Past Performance of Water flooding in Weyburn Oil Field”, SPE pp. 99789, 2006.
 Peramanus et al, “Flow loop apparatus to study the effect of solvent temperature and additives on asphaltene precipitation” Journal of Petroleum Science and Engineering, 1999.
 Hirschberg A., “Influence of temperature and pressure on asphaltene flocculation” SPE, June 1984.
 Srivastava R.S. & Huang S.S., “Asphaltene Deposition during CO2 Flooding: A Laboratory Assessment”, SPE, p. 37468, 1997
 Takahashi S., Hayashi Y., Yazawa N. & Sarma, “Characteristics and Impact to asphaltene Precipitation during CO2 Sandstone and Carbonate Cores: An Investigative Analysis through Laboratory Tests and CompositionalSimulation”, SPE, pp. 84895, 2003.
 kokal S., Al-Ghamdi A. & Krinis D., “Asphaltene Precipitation in High Gas-Oil Ratio Wells”, Saudi Aramco Journal of Technology, 2005 .
 Menhaj M., Neural Network, first copy, tertiary printing, AmirKabir University Publications.
 Kennedy J., & Eberhart R., “Particle Swarm Optimization,” IEEE International Conference on Networks,