Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm

One of the most important phenomena in petroleum industry is the precipitation of heavy organic materials such as asphaltene in oil reservoirs, which can cause diffusivity reduction and wettability alteration in reservoir rock and finally affect oil production and economical efficiency. In this work, the model based on a feed-forward artificial neural network (ANN) optimized by imperialist competitive algorithm (ICA) to predict of asphaltene precipitation is proposed. ICA is used to decide the initial weights of the neural network. The ICA–ANN model is applied to the experimental data reported in the literature. The performance of the ICA–ANN model is compared with Scaling model and conventional ANN model. The results demonstrate the effectiveness of the ICA–ANN model.