Abstract
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.
Highlights
5000 indicator diagrams with insufficient liquid supply were collected from the oilfield site, and a sample set was established after preprocessing; 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; 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
This paper proposes an intelligent recognition method of oil well fluid supply shortage based on a convolutional neural network
Summary
On-site personnel, based on experience, can intuitively judge the degree of insufficient liquid supply reflected in the power diagram, but it is difficult to accurately quantify. Based on the above analysis, it can be predicted that analyzing the degree of insufficient fluid supply by using a convolutional neural network in oil wells has great potential. This paper proposes an intelligent recognition method of oil well fluid supply shortage based on a convolutional neural network. The indicator diagram [11], the corresponding convolutional neural network architecture is researched and designed, and the recognition model is constructed in combination with the Softmax classifier. Experiments show that the accuracy of the model is over 98%, which provides effective methods and tool support for the accurate and efficient analysis of insufficient fluid supply in oil wells
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