Study On Wax Deposition Model Of Wax Crude Oil Pipeline

In china most oil are high wax content oil, it has the characteristics of high solidification point and high wax precipitation point. The hot oil following in the pipeline was influenced by environment and the oil temperature reduces. When the oil temperature falls below wax precipitation point, wax crystals appear. When wax molecules in oil are saturated they will move to pipeline wall under molecular diffusion effect. Eventually they deposit on the pipeline wall. As time goes on the deposition on the wall increase, the flow area in pipeline decrease and flow resistance enlarge. Seriously it will cause blockage. Therefore the study of the law of wax content oil in pipeline is necessary, it has important significance for pipeline security operation. This paper mainly studied the law of wax deposition on the basis of literature research and experimental study. Besides wax deposition models were established by using regression analysis method and RBF artificial neural network method. Finally, the two models were analyzed as well as compared with Huang qiyu model and Wang jifeng model. The following are research contents and conclusions.(1) Theoretical research on wax deposition mechanism, experimental apparatus, influence factors and wax deposition model has been conducted.(2) In accordance with the high wax content demand, choose oil samples A7and A10from some high waxy crude oil production wells in Bohai as experimental oil. Basic physical property analysis have been conducted mainly include wax content, condensation point, wax precipitation point, wax peak and viscosity-temperature curve.(3) According to literature research, the indoor wax deposition experimental apparatus has been designed and made on the basis of cold-finger theory.(4) The influence of oil temperature, temperature difference between oil and pipeline wall and flow velocity on wax deposition rate has been studied. The study indicated that wax deposition decrease when oil temperature increase and temperature difference increase the wax deposition will increase. When the flow regime is turbulent flow, flow rate increases the wax deposition would reduce.(5) Wax deposition model has been established by using regression analysis, on the other side, the model error has been compared with Huang qiyu model and Wang jifeng model. Through the comparing result we found that the error of the model in this paper is smaller and it is better.(6) Wax deposition model has been established by using RBF artificial neural network. On the other hand the model has been used to forecast wax deposition rate and compared it with the experimental result. In addition the model build by RBF artificial neural network has been compared with the model build by regression analysis, the result proved the model built by RBF artificial neural network is better.