Shaohua Gu1,2, Jiabao Wang3, Liang Xue3,*, Bin Tu3, Mingjin Yang3, Yuetian Liu3
CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1579-1599, 2022, DOI:10.32604/cmes.2022.019435
Abstract Production performance prediction of tight gas reservoirs is crucial to the estimation of ultimate recovery, which
has an important impact on gas field development planning and economic evaluation. Owing to the model’s
simplicity, the decline curve analysis method has been widely used to predict production performance. The
advancement of deep-learning methods provides an intelligent way of analyzing production performance in tight
gas reservoirs. In this paper, a sequence learning method to improve the accuracy and efficiency of tight gas
production forecasting is proposed. The sequence learning methods used in production performance analysis
herein include the recurrent neural network (RNN), long… More >
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