Well Production Real-Time Intelligent Monitoring Based on Convolutional Neural Network

DOI: 10.14800/IOGR.453

Abstract

Based on the theory of deep learning, this paper proposes the use of convolution neural network (CNN) method to identify the working condition of pumping unit of an oil well by indicator diagram. The structure and principle of CNN are introduced, and the CNN-based indicator diagram identification model is established. Over 180,000 pieces of indicator diagram data from a real oilfield are collected and the working conditions corresponding to each indicator diagram are manually labeled as the training set for CNN model. Using this training set, the CNN-based indicator diagram identification model is trained and tested. The training and test results show that the accuracy of the CNN-based indicator diagram identification model is more than 90%. Compared with the traditional neural network models, CNN model can learn from the image directly and avoid the complex process of artificial extraction of features, hence lead to a better performance. The CNN-based indicator diagram classification and recognition model can be combined with the real-time data acquisition system of the well to realize the real-time intelligent monitoring of the oil well working condition.