页岩气
石油工程
油页岩
人工神经网络
体积热力学
生产(经济)
生产力
磁导率
地质学
计算机科学
人工智能
化学
物理
宏观经济学
古生物学
量子力学
经济
生物化学
膜
作者
Xianchao Chen,Li Jiang,Ping Gao,Jingchao Zhou
标识
DOI:10.1080/10916466.2022.2032739
摘要
The exploration and development of shale gas is becoming more important owing to the increasing of world energy demand. However, calculating the productivity of horizontal wells after shale gas volume fracturing is always difficult due to various complicated factors. In this study, the long short-term memory (LSTM) neural network was establised and demonstrated to be successful in China complex shale gas production time series prediction. Firstly, the geological characteristics of shale gas and fracturing technology was briefly introduced. Then, a shale gas horizontal well volume fracturing productivity prediction model was established based on a long short-term memory (LSTM) neural network and using actual production data for two shale gas models. The mean absolute percentage error between the predicted results and the actual production data is less than 5%, which indicates a good performance in terms of the prediction of values and trends. Based on this model, sensitivity analysis of the effect of the stimulated reservoir volume (SRV), fracture parameters, permeability, and other factors on the productivity of shale gas wells was carried out. The newly developed LSTM time series productivity prediction method and the insights it provides can be used by reservoir engineers to optimize shale gas field development plans.HighlightsA new machine learning (LSTM) shale gas production prediction model is proposed.The new machine learning model is better than the traditional RTA or DCA methods.The example calculation results show that the LSTM can predict the future production capacity value with a certain accuracy.The new model is useful for optimization in shale gas field development.
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