In this study, we developed and implemented an expert system capable of predicting asphaltene precipitation potential. We, therefore, introduce a new approach for predicting asphaltene precipitation caused by solubility incompatibility between crude and solvent, and due to the imbalance between various intermolecular forces between resins and colloidal asphaltene particles. The system was constructed as three modules, on the basis of refractive index, solubility, and rules of thumb pertaining to asphaltene precipitation generated from field experience. Reasoning was conducted using fuzzy logic, in which asphaltene precipitation was predicted independently by the three modules, and then an overall conclusion was aggregated. The system is capable of estimating the potential of asphaltene precipitation for a wide range of phase-behavior reservoir conditions, and for a much-wider range of live and stock-tank crude compositions than what can normally be handled by a human expert.