The Precipitation and deposition of crude oil polar fractions such as asphaltenes in petroleum reservoirs reduces considerably the rock permeability and the oil recovery. Therefore, it is of great importance to determine "how" and "how much" the asphaltenes precipitate as a function of pressure, temperature and liquid phase composition. In present work, an Artificial Neural Network (ANN) model was designed and applied to predict the amount of asphaltene precipitation at a given operating condition. An extensive experimental data for the amount of asphaltene precipitation at various temperatures (293-343 K) was used to create the input and target data for generating the ANN model. The predicted results of asphaltene precipitation from ANN model was also compared with the results of some proposed scaling equations. The results revealed that scaling equations fail to predict the amount of asphaltene precipitation adequately for different ranges of temperature and dilution ratio, especially at lower values of dilution ratio. While an acceptable agreement between experimental data and predicted amount of asphaltene precipitation for all ranges of dilution ratio, solvent molecular weight and temperature was obtained through using ANN model.

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