Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 599-603.

• Interdiscipline & Application • Previous Articles     Next Articles

Study on Optimized Method for Predicting Paraffin Deposition of Pumping Wells Based on SCRF

WANG Li-jun, ZHI Zhi-ying, JIA Lu, LI Wei   

  1. (Data Company of Petrochina Xinjiang Oilfield Company,Karamay,Xinjiang 834000,China)
  • Online:2019-11-10 Published:2019-11-20

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

CLC Number: 

  • TP3
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