Author(s)

Yanfeng He^1*, Zhenlong Wang¹, Bin Liu², Xiang Wang¹, Bingchao Li¹

¹School of Petroleum Engineering, Changzhou University, Changzhou, China.
²The Eighth Oil Production Plant, Petrochina Changqing Oilfield Branch, Xi’an, China.

Traditional methods for judging the degree of insufficient fluid supply in oil wells have low efficiency and limited accuracy. To address this problem, a method for intelligently identifying the degree of insufficient fluid supply in oil wells based on convolutional neural networks is proposed in this paper. Firstly, 5000 indicator diagrams with insufficient liquid supply were collected from the oilfield site, and a sample set was established after preprocessing; then based on the AlexNet model, combined with the characteristics of the indicator diagram, a convolutional neural network model including 4 layers of convolutional layers, 3 layers of down-pooling layers and 2 layers of fully connected layers is established. The backpropagation, ReLu activation function and dropout regularization method are used to complete the training of the convolutional neural network; finally, the performance of the convolutional neural network under different iteration times and network structure is compared, and the super parameter optimization of the model is completed. It has laid a good foundation for realizing the self-adaptive and intelligent matching of oil well production parameters and formation fluid supply conditions. It has certain application prospects. The results show that the accuracy of training and verification of the method exceeds 98%, which can meet the actual application requirements on site.

Degree of Insufficient Fluid Supply in Oil Wells, Indicator Diagram, Convolutional Neural Network, Alexnet, Backpropagation Algorithm, ReLu Activation Function, Dropout Regularization

He, Y. , Wang, Z. , Liu, B. , Wang, X. and Li, B. (2021) Intelligent Recognition Method of Insufficient Fluid Supply of Oil Well Based on Convolutional Neural Network. Open Journal of Yangtze Oil and Gas, 6, 116-128. doi: 10.4236/ojogas.2021.63011.