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A PSO-LSSVM Based Petroleum Production Model for Production-Injection Wells System
Cheng HB(程海波); Zhang LT(张立婷); Ling X(凌霄); Zeng P(曾鹏); Yu HB(于海斌)
作者部门工业控制网络与系统研究室
会议名称2017 Chinese Automation Congress (CAC2017) & China Intelligent Manufacturing International Conference (CIMIC2017)
会议日期October 20-22, 2017
会议地点Jinan, China
会议录名称2017 Chinese Automation Congress (CAC2017) & China Intelligent Manufacturing International Conference (CIMIC2017)
出版者IEEE
出版地New York
2017
页码5770-5775
收录类别EI ; CPCI(ISTP)
EI收录号20183005603803
WOS记录号WOS:000427816105162
产权排序1
ISBN号978-1-5386-3524-7
关键词Petroleum Production Water Flooding Oilfield Production-injection Wells System Least Squares Support Vector Machine (Lssvm) Particle Swarm Optimization (Pso)
摘要

In water flooding oilfield, petroleum production is the most crucial target for production-injection wells system. An effective, informative and accurate production prediction facilitates parameter adjustment, production optimization, fault analysis and decrease in production cost. Some effective Artificial Intelligence (Al) technologies have been widely used in various kinds of industrial fields including manufacturing, building, chemistry and medicine. In this paper, AI technologies based on least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO) are applied to model petroleum production. Finally, the proposed method is verified by using the production data from Liaohe oilfield. Empirical results show that the proposed modeling approach is more feasible and accurate than other approaches.

语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/21322
专题工业控制网络与系统研究室
通讯作者Cheng HB(程海波)
作者单位1.Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
推荐引用方式
GB/T 7714
Cheng HB,Zhang LT,Ling X,et al. A PSO-LSSVM Based Petroleum Production Model for Production-Injection Wells System[C]. New York:IEEE,2017:5770-5775.
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