Investigation of wax precipitation in crude oil: Experimental and modeling

In this work, a series of experiments were carried to investigation of rheological behavior of crude oil using waxy crude oil sample in the absence/presence of flow improver such as ethylene-vinyl acetate copolymer. The rheological data covered the temperature range of 5–30 °C. The results indicated that the performance of flow improver was dependent on its molecular weight. Addition of small quantities of flow improver, can improve viscosity and pour point of crude oil. Also, an Artificial Neural Network (ANN) model using Multi-Layer Perceptron (MLP) topology has been developed to account wax appearance temperature and the amount of precipitated wax and the model was verified using experimental data given in this work and reported in the literature. In order to compare the performance of the proposed model based on Artificial Neural Network, the wax precipitation experimental data at different temperatures were predicted using solid solution model and multi-solid phase model. The results showed that the developed model based on Artificial Neural Network can predict more accurately the wax precipitation experimental data in comparison to the previous models such as solid solution and multi-solid phase model with AADs less than 0.5%. Furthermore, the number of parameters required for the Artificial Neural Network (ANN) model is less than the studied thermodynamic models.