基于IFOA-KELM-MEA模型的游梁式抽油机采油系统井下工况的短期预测  

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

作　　者：李琨[1] 韩莹[1] 佘东生[1] 魏泽飞[1] 黄海礁[2] 

机构地区：[1]渤海大学工学院,辽宁锦州121013 [2]辽河油田分公司锦州采油厂采油作业五区,辽宁锦州121209

出　　处：《化工学报》2017年第1期188-198,共11页CIESC Journal

基　　金：国家自然科学基金项目(61403040)~~

摘　　要：实现对井下工况的预测是及时掌握抽油井生产状态的有效方法,对提高油井生产效率和降低维护成本具有十分重要的意义。采用混沌理论实现抽油井井下工况的短期预测,首先将所提取的示功图的不变曲线矩特征向量作为预测变量,在证明其数据序列具有混沌特性后,由核极限学习机(kernel extreme learning machine,ELM)建立混沌时间序列预测模型,对其中的几个不确定参数采用改进的果蝇优化算法(improved fruit fly optimization algorithm,IFOA)进行优化选取,IFOA采用全局群体多样进化和局部个体随机变异的策略,最后,对模型所预测的结果进行物元分析(matter-element analysis,MEA),诊断其属于哪种故障类型。由某油田作业区的两口生产井进行实例验证,结果表明所提出的IFOA-KELM-MEA预测模型是合理有效的。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.

关 键 词：混沌时间序列预测 游梁式抽油机 核极限学习机 果蝇优化算法 物元分析 测量 石油 模型 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]