摘要
油井的实时生产监测对油田的辅助生产和精细化管理有重要意义。然而,针对仅有小样本生产数据、数据波动大且有缺失的特高含水期油井,传统的机器学习算法无法实现良好的生产预测。提出一种基于卷积神经网络和迁移学习的多任务生产预测方法。该方法不仅可以实现时间和空间上特征的自适应提取,还可以改善模型在小样本数据上的预测性能。结果表明:相比于基准模型,产液量和动液面的平均绝对误差分别降低31.26%和60.81%,决定系数分别提高1.89%和7.59%。基于迁移学习的MTCNN模型提高小样本数据油井的生产预测精度,实现了特高含水油井产液量和动液面的实时预测,对抽油机系统的效率优化、油井边缘设备智能化有参考意义。
The real-time production monitoring of oil wells is of great significance for enhancing auxiliary production and fine management in oil fields.However,the traditional machine learning algorithms struggle to provide accurate production predic-tions for ultra-high water cut oil fields due to limited sample production data,substantial data fluctuations,and missing data.This paper proposes a multi-task production forecasting scheme based on convolutional neural networks and transfer learning to address these challenges.This model not only enables adaptive extraction of temporal and spatial features,but also enhanc-esprediction performance on small sample data.The experimental results demonstrate notable improvements over the bench-mark model.Specifically,the average absolute percentage errors of liquid production and dynamic liquid level are reduced by 31.26%and 60.81%respectively.Additionally,and the determination coefficient increases by 1.89%and 7.59%respec-tively.The MTCNN model,based on transfer learning,enhances the prediction accuracy of oil wells with limitedsample data,enabling real-time prediction of liquid production and dynamic liquid level inultra-high water cut oil wells.It holds significant implications for the efficiency optimization of pumping unit systems and the intelligence of oil well edge equipment.
作者
姜春雷
方硕
刘伟
邵克勇
陈朋
JIANG Chunei;FANG Shuo;LIU Wei;SHAO Keyong;CHEN Peng(School of Electrical Information Engineering,Northeast Petroleum University,Daqing 163318,China;Sanya Offshore Oil&Gas Research Institute,Northeast Petroleum University,Sanya 572024,China)
出处
《中国石油大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2023年第6期162-170,共9页
Journal of China University of Petroleum(Edition of Natural Science)
基金
黑龙江省自然科学基金项目(LH2021F008)
海南省重点研发项目(ZDYF2022SHFZ047)
控制科学与工程团队专项(2022TSTD-04)。
关键词
卷积神经网络
迁移学习
特高含水油井
小样本数据
多任务
动态生产预测
convolutional neural network
transfer learning
extra high water cut oil well
small sample data
multitasking
dynamic production forecast
作者简介
第一作者:姜春雷(1977-),男,教授,博士,博士生导师,研究方向为人工智能、复杂系统检测与预诊断。E-mail:jiangchunlei_nepu@163.com;通信作者:刘伟(1971-),男,教授,博士,博士生导师,研究方向为故障诊断与智能系统。E-mail:442780146@qq.com。
