Open Access
ARTICLE
Deep-Learning-Based Production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models
Shaohua Gu1,2, Jiabao Wang3, Liang Xue3,*, Bin Tu3, Mingjin Yang3, Yuetian Liu3
1 State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing, 100083, China
2 Sinopec Petroleum Exploration and Production Research Institute, Beijing, 100083, China
3 Department of Oil-Gas Field Development Engineering, College of Petroleum Engineering, China University of Petroleum, Beijing,
102249, China
* Corresponding Author: Liang Xue. Email:
(This article belongs to the Special Issue: Modeling of Fluids Flow in Unconventional Reservoirs)
Computer Modeling in Engineering & Sciences 2022, 131(3), 1579-1599. https://doi.org/10.32604/cmes.2022.019435
Received 24 September 2021; Accepted 24 November 2021; Issue published 19 April 2022
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.
Keywords
Cite This Article
APA Style
Gu, S., Wang, J., Xue, L., Tu, B., Yang, M. et al. (2022). Deep-learning-based production decline curve analysis in the gas reservoir through sequence learning models. Computer Modeling in Engineering & Sciences, 131(3), 1579-1599. https://doi.org/10.32604/cmes.2022.019435
Vancouver Style
Gu S, Wang J, Xue L, Tu B, Yang M, Liu Y. Deep-learning-based production decline curve analysis in the gas reservoir through sequence learning models. Comput Model Eng Sci. 2022;131(3):1579-1599 https://doi.org/10.32604/cmes.2022.019435
IEEE Style
S. Gu, J. Wang, L. Xue, B. Tu, M. Yang, and Y. Liu "Deep-Learning-Based Production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models," Comput. Model. Eng. Sci., vol. 131, no. 3, pp. 1579-1599. 2022. https://doi.org/10.32604/cmes.2022.019435