电子测量与仪器学报

王通,段泽文,李琨.基于改进AdaBoost的油井动液面自适应集成建模[J].电子测量与仪器学报,2017,31(8):1342-1348

基于改进AdaBoost的油井动液面自适应集成建模

Adaptive ensemble modeling for dynamic liquid level of oil well based on improved AdaBoost method

DOI：10.13382/j.jemi.2017.08.025

英文关键词:ensemble learning algorithm dynamic modeling error rate dynamic liquid level generalization ability

基金项目:辽宁省博士科研启动基金（201601163）、国家自然科学基金（61403040）资助项目

作者	单位
王通	沈阳工业大学电气工程学院沈阳110870
段泽文	沈阳工业大学电气工程学院沈阳110870
李琨	渤海大学工学院锦州121013

Author	Institution
Wang Tong	School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Duan Zewen	School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Li Kun	College of Engineering,Bohai University, Jinzhou 121013, China

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中文摘要:

针对单一模型软测量算法在动液面预测应用过程中存在泛化能力弱、自适应性差等问题，提出利用AdaBoost集成学习的思想，突出预测误差在建模样本权重及弱学习机权重中的作用，使之更加适合回归模型预测。针对油井工况动态多变导致软测量模型随生产进行逐渐失效的问题，提出利用油田生产过程中定期巡检的少量动液面数据评估原有集成模型，利用相似度原理在保留原有模型信息的基础上增加新信息，并在此基础上根据新样本权重更新弱学习机模型，集成为强学习机模型以动态适应新的油田生产工况。通过对油田生产现场实际数据验证结果表明，该方法对油田生产波动的自适应能力强，能够提高动液面软测量模型的泛化能力及预测精度。

英文摘要:

When the single soft sensor model is used for the dynamic liquid level prediction, there are many shortcomings such aspoor generalization ability, weak adaptive ability, etc. In order to solve these problems, a soft sensor modeling method based on AdaBoost ensemble learning algorithm is proposed in this paper. The proposed method focuses on effects of the prediction error to the weight of the modeling samples and weak learning machine, therefore which is more suitable for the regression model prediction.In practical production,dynamic and changing working conditions during operations may lead to failure of the soft sensor model. In order to solve this problem,a small amount of patrol measuring data of the dynamic liquid levelis used to evaluate the original model, and then the similarity principle is used to add new data on the basis of the original model. And on this basis the weight of the new data is used to update the weak learning machine to become the strong learning machine model to dynamically adapt to the new production conditions.The simulation results using the real operation data of the oil well show that the proposed method has strong adaptive ability for fluctuation in production and can improve the generalization ability and the prediction accuracy of the soft sensor model.

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