IFOA-KELM-MEA model based transient prediction on down-hole working conditions of beam pumping units

被引：0
|
作者：
Li K. [1 ]
Han Y. [1 ]
She D. [1 ]
Wei Z. [1 ]
Huang H. [2 ]
机构：
[1] College of Engineering, Bohai University, Jinzhou, 121013, Liaoning
[2] The Fifth District of Jinzhou Oil Production Plant, Liaohe Oilfield Company, Jinzhou, 121209, Liaoning
来源：
Li, Kun (bhulikun@163.com) | 1600年 / Materials China卷 / 68期
基金：
中国国家自然科学基金;
关键词：
Beam pumping units; Chaotic time series prediction; Fruit fly optimization algorithm; Kernel extreme learning machine; Matter-element analysis; Measurement; Model; Petroleum;
D O I：
10.11949/j.issn.0438-1157.20160834
中图分类号：
学科分类号：
摘要：
Prediction for down-hole working conditions of beam pumping units is an effective strategy to timely control oil well's working state, and is important to improve production efficiency and to reduce maintenance cost. Chaos theory was used in transient prediction for oil well's down-hole working conditions. First, moment eigenvalues of invariant curves were extracted from dynamometer chart as predictive variables. Then, after data sequence of these predictive variables were proved to have chaotic characteristics, chaotic time series prediction model was established by ELM (kernel extreme learning machine) method and several uncertain variables of the model were optimally solved by IFOA (improved fruit fly optimization algorithm) with two strategies of global population diversity-evolution and local individual random-variation. Finally, model predictive results were analyzed to determine fault types according to MEA (matter-element analysis) method. Case study of two oil wells in one oilfield showed that the IFOA-KELM-MEA prediction model was reasonable and effective. © All Right Reserved.
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页码：188 / 198
页数：10
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