自适应软测量方法在动液面预测中的研究与应用

doi:10.3969/j.issn.0438-1157.2014.12.034

化工学报 ›› 2014, Vol. 65 ›› Issue (12): 4898-4904.DOI: 10.3969/j.issn.0438-1157.2014.12.034

自适应软测量方法在动液面预测中的研究与应用

王通^1,2, 高宪文¹, 刘文芳²

1. 东北大学信息科学与工程学院, 辽宁沈阳 110819;
2. 沈阳工业大学电气工程学院, 辽宁沈阳 110819

收稿日期:2014-05-20 修回日期:2014-08-15 出版日期:2014-12-05 发布日期:2014-12-05
通讯作者: 王通
基金资助:
国家自然科学基金重点项目(61034005).

Adaptive soft sensor method and application in determination of dynamic fluid levels

WANG Tong^1,2, GAO Xianwen¹, LIU Wenfang²

1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, Liaoning, China;
2. School of Electrical Engineering, Shenyang University of Technology, Shenyang 110819, Liaoning, China

Received:2014-05-20 Revised:2014-08-15 Online:2014-12-05 Published:2014-12-05
Supported by:
supported by the Key Program of the National Natural Science Foundation of China(61034005).

摘要/Abstract

摘要： 针对传统人工检测方法在测量动液面时存在精度低、实时性差等问题,采用软测量技术来完成对动液面的测量工作.根据对现场数据特性的分析,提出采用经验模态分解和基于黑洞的最小二乘支持向量机预测相结合的算法来实现动液面软测量建模;通过构建模型性能评价模块,动态更新模型,解决在油田生产过程中,静态模型不能完全反映生产工况导致模型失效的问题,提高算法的自适应能力及预测量精度.最后通过对油田生产现场监测数据进行实验验证,结果表明,该方法对油田动液面测量精度高,对生产波动的自适应能力强,满足油田现场测试使用要求,提高油田生产自动化程度.

关键词: 主元分析, 动态建模, 优化, 经验模态分解, 动液面, 最小二乘支持向量机

Abstract: For the measurement of dynamic liquid level of oil wells, the traditional manual work has many shortcomings, such as low precision, poor real-time performance etc. According to analysis of actual production data, a soft sensor method based on empirical mode decomposition (EMD) and black hole-least squares support vector machine (BH-LSSVM) was proposed to realize soft sensor modeling of dynamic liquid level. In oilfield production, static model cannot fully reflect the production conditions which may lead to model failure. Therefore a dynamic model was proposed by building a performance evaluation module, which could improve adaptive ability and prediction precision. The proposed method had higher measurement accuracy of dynamic liquid level and stronger adaptive ability for production fluctuation, which met requirement of oil production. Automation in oil production was improved.

Key words: principal component analysis, dynamic modeling, optimization, EMD, dynamic fluid level, LSSVM

中图分类号:

TQ063

王通, 高宪文, 刘文芳. 自适应软测量方法在动液面预测中的研究与应用[J]. 化工学报, 2014, 65(12): 4898-4904.

WANG Tong, GAO Xianwen, LIU Wenfang. Adaptive soft sensor method and application in determination of dynamic fluid levels[J]. CIESC Journal, 2014, 65(12): 4898-4904.

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自适应软测量方法在动液面预测中的研究与应用

Adaptive soft sensor method and application in determination of dynamic fluid levels

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