@Article{cmes.2022.019435,
AUTHOR = {Shaohua Gu, Jiabao Wang, Liang Xue, Bin Tu, Mingjin Yang, Yuetian Liu},
TITLE = {Deep-Learning-Based Production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {131},
YEAR = {2022},
NUMBER = {3},
PAGES = {1579--1599},
URL = {http://www.techscience.com/CMES/v131n3/47403},
ISSN = {1526-1506},
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 short-term memory (LSTM) neural network, and gated
recurrent unit (GRU) neural network, and their performance in the tight gas reservoir production prediction is
investigated and compared. To further improve the performance of the sequence learning method, the hyperparameters in the sequence learning methods are optimized through a particle swarm optimization algorithm,
which can greatly simplify the optimization process of the neural network model in an automated manner. Results
show that the optimized GRU and RNN models have more compact neural network structures than the LSTM
model and that the GRU is more efficiently trained. The predictive performance of LSTM and GRU is similar,
and both are better than the RNN and the decline curve analysis model and thus can be used to predict tight gas
production.},
DOI = {10.32604/cmes.2022.019435}
}