Title :
Application of dynamic liquid level prediction model based on improved SVR in sucker rod pump oil wells
Author :
Hou Yanbin ; Gao Xianwen ; Wang Mingshun ; Li Xiangyu ; Liu Yu ; Wu Bing
Author_Institution :
Coll. of Inf. & Sci. & Eng., Northeastern Univ., Shenyang, China
Abstract :
This study built a dynamic liquid level prediction model based on improved SVR (Support Victor Regression) in sucker rod pump wells. This modeling method adopted sliding window to limit the number of samples and applied genetic algorithm to realize automatic optimization of C ande which are parameters of SVR. Through the simulation experiment, we verified the effectiveness of the modeling method and improved the precision of the model. After a period of actual operating in some oilfield, good results were obtained and the precision could perfectly meet the requirement of oil production.
Keywords :
fuel pumps; genetic algorithms; level measurement; oil technology; production engineering computing; regression analysis; support vector machines; automatic optimization; dynamic liquid level prediction model; genetic algorithm; improved SVR; oil production requirement; sliding window; sucker rod pump oil wells; support vector regression; Data models; Genetic algorithms; Liquids; Optimization; Predictive models; Support vector machines; Training; Dynamic fluid; GA; SVR; Sliding Window;
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese
Conference_Location :
Xi´an
