基于SCRF的抽油井结蜡预测方法优化研究

摘要/Abstract

摘要： 在油田生产过程中,油井受各种因素的影响容易发生结蜡。油井结蜡通常会降低油井产生,造成油井阻塞,甚至会造成停井及烧电机等现象,大大增加采油成本。对抽油井结蜡状态进行提前预测,实现抽油井设备预见性维护对油田降本增效及智能化管理具有重要意义。针对基于不平衡数据集构建结蜡预测模型预测效果不理想的问题,文中提出了一种面向非平衡数据的集成学习方法SCRF(SMOTE CLUSTER RANDOM FOREST)。该方法首先使用SMOTE方法对原数据集中的少数类进行过采样以增加少数类的数量,缩小不平衡比例;然后对新的数据集采用CLUSTER聚类方法分层欠采样,生成训练数据集;最后采用基于bagging技术的随机森林算法对训练数据集进行集成学习,从而生成预测模型。实验结果表明,样本均衡后模型预测效果更佳,预测精度和效率都有一定程度的提高。

关键词: 不平衡数据分类, 集成学习, 结蜡预测模型, 样本均衡

Abstract: In the production process of oil field,paraffin deposition is easy to occur for oil wells affected by various factors.Paraffin deposition usually causes blockage of oil wells,and even causes well stuck or overload burning of electric motors,which will greatly reduce oil well production and increase the cost of oil production.So predicting the paraffin deposition state of pumping wells in advance and realizing predictive maintenance for pumping wells equipment,can reduce the cost and increase efficiency for oil fields,which have great significance on intelligent management.In order to improve the accuracy of paraffin deposition prediction based on unbalanced data set for pumping wells,this paper proposed an integrated learning method named SCRF for unbalanced data.Firstly,SMOTE method is used to oversample a few classes in the original data set to increase the number of minority classes and reduce the unbalanced proportion.Then CLUSTER clustering method is used to stratify and undersample the new data set to generate the training data set.Finally,random forest algorithm based on bagging technology is used to integrate the training data set,so as to ge-nerate the prediction model.The experimental results show that the prediction effect of the model is better after sample equalization,whilethe prediction efficiency and accuracy are improved to a certain extent.

Key words: Integration algorithm, Paraffindeposition prediction model, Sample balance processing, Unbalance dataset classification

中图分类号:

王利君, 支志英, 贾鹿, 李伟. 基于SCRF的抽油井结蜡预测方法优化研究[J]. 计算机科学, 2019, 46(11A): 599-603. https://doi.org/

WANG Li-jun, ZHI Zhi-ying, JIA Lu, LI Wei. Study on Optimized Method for Predicting Paraffin Deposition of Pumping Wells Based on SCRF[J]. Computer Science, 2019, 46(11A): 599-603. https://doi.org/

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