页岩气
人工神经网络
油页岩
石油工程
生产(经济)
反向传播
非线性系统
地质学
计算机科学
环境科学
人工智能
经济
量子力学
物理
宏观经济学
古生物学
作者
Jiafeng Li,Hui Hu,Xiang Li,Jin Qian,Tianhao Huang
摘要
Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods
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