Summary To improve the overall performance of continuous spiral baffle heating systems, we propose in this paper two different structural models of electric heaters for in-situ shale oil wells. The models are simulated using Fluent software to investigate the flow and heat-transfer characteristics under different mass flow rates. The variation in heat-transfer coefficient, pressure drop, and overall performance of the heating plate under different gas mass flow rates and heights of heating and shielding plates are analyzed. The performance of the two different heater structures is compared with Wang’s laboratory experiment. The results show that Model I of the heating system has the best overall performance when the gas mass flow rate is between 9.74×10−3 kg/s and 1.624×10−2 kg/s, and the height of the heating and shielding plates is 35 mm at a mass flow rate of 9.74×10–3 kg/s. Wang’s pressure drop (ΔP) is more than 2.48 times higher than that of Model I and more than 6.49 times higher than that of Model II, while the heat-transfer coefficient (h) of both Model I and Model II is increased by more than 15% compared to Wang’s experiment. The overall performance (g) of Model II is increased by more than 5.7 times compared to Wang’s experiment, and the overall performance (g) of Model II is increased by more than 1.68 times compared to Model I. These results provide a theoretical basis for optimizing the design of continuous spiral baffle heating systems.

This paper discussed the fundamental contents and the process developed of the GM (1,1) forecast and the dynamic gray forecast model, and predicted the vibration state of electrical submersible pump through the above-mentioned two models in the electrical submersible pump unit of oilfield. Vibration state was described by the vibration acceleration signals. The result showed that the precision of tow models was greater than 98%, prediction error was less than 5%, and the two kinds of gray forecast model were successfully applied in vibration state forecast for electrical submersible pump. The effect of dynamic gray forecast model was better than the GM (1,1) forecast model because that it added the new information, increased the white grade of gray interzone, and had the tracking feature. This gray forecast model is simple, high precision and easily calculated in computer.

No abstract

No abstract

No abstract