Production prediction of extra high water cut oil well based on convolution neural network and transfer learning

Key words:  convolutional neural network  transfer learning  extra high water cut oil well  small sample data  multitasking  dynamic production forecast

Abstract:

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 predictions 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 enhancesprediction performance on small sample data.The experimental results demonstrate notable improvements over the benchmark 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% respectively.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.