A meta-learning fusion method for monitoring data prediction of oil wells

Abstract

The advent of the oilfield Internet of Things (IoT) has led to widespread sensor deployment on oil wells, enabling real-time data acquisition and monitoring. The data exhibits highly nonlinear characteristics, posing prediction challenges. Prediction models for specific wells often lack generalization and are not suitable for other wells due to geological and working condition differences. In this study, a meta-learning fusion method based on model-agnostic meta-learning (MAML), convolutional neural networks (CNN), long short-term memory networks (LSTM), and multi-head self-attention mechanisms (MHSA) is proposed for monitoring data prediction tasks. This method employs MAML for generalization enhancement via multi-task training, addressing prediction challenges with limited data. CNN captures local features, LSTM handles temporal dependencies, and MHSA enhances complex data correlation capture. Taking the prediction of oil well dynamometer card data as an example, the experimental results demonstrate that, compared with existing methods, this approach exhibits higher prediction accuracy and generalization capability. Specifically, the MSE loss is reduced by 35 %–72 % when compared to the three existing prediction methods mentioned in this study. Prediction error grows with forecast horizon, highlighting potential for improvement in long-term accuracy. This method provides valuable insights for time series forecasting in the petroleum and related industries.