Application of dynamic liquid level prediction model based on improved SVR in sucker rod pump oil wells

被引：0
|
作者：
Hou Yanbin [1 ]
Gao Xianwen [1 ]
Wang Mingshun [1 ]
Li Xiangyu [1 ]
Liu Yu [1 ]
Wu Bing [2 ]
机构：
[1] Northeastern Univ, Coll Informat & Sci & Engn, Shenyang 110819, Peoples R China
[2] Supervis & Testing Ctr Qual Technol, Sanmenxia 472000, Peoples R China
来源：
2013 32ND CHINESE CONTROL CONFERENCE (CCC) | 2013年
基金：
中国国家自然科学基金;
关键词：
SVR; Sliding Window; GA; Dynamic fluid;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 and epsilon 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.
引用
收藏
页码：7826 / 7830
页数：5
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