文章基本信息

标题：A NEW METHOD FOR GAS WELL LIQUID ACCUMULATION PREDICTING BASED ON CONVOLUTIONAL NEURAL NETWORK ENVIRONMENT
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作者：Jingguo Du ; Kaijun Li ; Xinan Yu 等
期刊名称：Fresenius Environmental Bulletin
印刷版ISSN：1018-4619
出版年度：2020
卷号：29
期号：7
页码：5487-5496
语种：English
出版社：PSP Publishing
摘要：Accurate prediction of liquid accumulation in gas wells is essential for efficient and stable gas field development. At present, the commonly used methods such as droplet model, liquid film model, stability analysis method and neural network method cannot well discriminate wellbore liquid accumulation. There is a large deviation between the predictions obtained by different liquid accumulation prediction models for different types of gas reservoirs. Because the liquid accumulation process is a continuous and dynamic process, the limitations of the commonly used models are mainly reflected in that the discrimination results can only represent the transient state of the liquid accumulation. In order to better solve the problem of liquid accumulation in gas wells, in this paper, we propose a new method for liquid accumulation prediction in gas wells based on convolutional neural network (CNN). This method reversely predicts the situation of liquid accumulation downhole through preprocessing, convolution, pooling, activation function (RELU, SoftMax), regularization and other processes. Finally, we verified it with examples. The results show that the convolutional neural network can predict liquid accumulation more accurately. Moreover, this model can detect liquid accumulation at the bottom of the well earlier than other models.
关键词：natural gas extraction;gas well liquid accumulation;convolutional neural network environment;image recognition;accurate prediction