石油钻采工艺2025，Vol.47Issue(1)：44-52,9.DOI:10.13639/j.odpt.202503013
面向多源数据的CNN-XGB抽油机井故障诊断技术
Research on CNN-XGB pumping well fault diagnosis for multi-source data
摘要
Abstract
In the process of oilfield production,the stable operation of pumping wells is crucial for improving production efficiency and economic benefits.However,most of the existing fault diagnosis techniques rely on a single data source(e.g.,schematic data or production parameters)for model training,and the diagnostic accuracy is seriously insufficient,or even the diagnostic failure occurs when faced with complex working conditions such as rod breakout and pump leakage.In this study,a CNN-XGB fault diagnosis model for multi-source data fusion is proposed.The model combines the convolutional neural network(CNN)and extreme gradient boosting(XGB)algorithms to extract the image features of the pump power diagram and the well production parameter features,respectively,to capture the feature information reflecting different working conditions from multiple angles.By integrating these features and inputting them into a multilayer perceptron(MLP),the model is able to achieve more accurate classification results,which significantly improves the specificity recognition capability.Experimental results show that the fusion model achieves more than 95%diagnostic accuracy and recall under six typical working conditions,demonstrating higher diagnostic accuracy and robustness compared to the traditional CNN and XGB models.This method effectively solves the limitation of a single data source in fault diagnosis,provides an innovative technical means for intelligent diagnosis of oilfield pumping well conditions,and has important practical application value and innovative significance.关键词
抽油机井/示功图/多源数据/卷积神经网络/极端梯度提升/模型融合/工况诊断Key words
sucker rod wells/dynamometer cards/multi-source data/convolutional neural network/extreme gradient lift/fusion model/fault diagnosis分类
石油、天然气工程引用本文复制引用
张黎明,吴雨垣,李敏,尹承哲,王鑫炎,刘冰,王树源..面向多源数据的CNN-XGB抽油机井故障诊断技术[J].石油钻采工艺,2025,47(1):44-52,9.基金项目
国家自然科学基金"油藏开发智能实时优化"(编号:52325402). （编号:52325402）
