Enhancing Production Prediction in Shale Gas Reservoirs Using a Hybrid Gated Recurrent Unit and Multilayer Perceptron (GRU-MLP) Model
Shale gas has revolutionized the global energy supply, underscoring the importance of robust production forecasting for the effective management of well operations and gas field development. Nonetheless, the intricate and nonlinear relationship between gas production dynamics and physical constraint...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/17/9827 |
| _version_ | 1827728270868611072 |
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| author | Xianlin Ma Mengyao Hou Jie Zhan Rong Zhong |
| author_facet | Xianlin Ma Mengyao Hou Jie Zhan Rong Zhong |
| author_sort | Xianlin Ma |
| collection | DOAJ |
| description | Shale gas has revolutionized the global energy supply, underscoring the importance of robust production forecasting for the effective management of well operations and gas field development. Nonetheless, the intricate and nonlinear relationship between gas production dynamics and physical constraints like shale formation properties and engineering parameters poses significant challenges. This investigation introduces a hybrid neural network model, GRU-MLP, to proficiently predict shale gas production. The GRU-MLP architecture can capture sequential dependencies within production data as well as the intricate nonlinear correlations between production and the governing constraints. The proposed model was evaluated employing production data extracted from two adjacent horizontal wells situated within the Marcellus Shale. The comparative analysis highlights the superior performance of the GRU-MLP model over the LSTM and GRU models in both short-term and long-term forecasting. Specifically, the GRU model’s mean absolute percentage error of 4.7% and root mean squared error of 120.03 are notably 66% and 80% larger than the GRU-MLP model’s performance in short-term forecasting. The accuracy and reliability of the GRU-MLP model make it a promising tool for shale gas production forecasting. By providing dependable production forecasts, the GRU-MLP model serves to enhance decision-making and optimize well operations. |
| first_indexed | 2024-03-10T23:27:56Z |
| format | Article |
| id | doaj.art-af1a5348b26140b4a96cdc91887adc54 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:27:56Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-af1a5348b26140b4a96cdc91887adc542023-11-19T07:52:18ZengMDPI AGApplied Sciences2076-34172023-08-011317982710.3390/app13179827Enhancing Production Prediction in Shale Gas Reservoirs Using a Hybrid Gated Recurrent Unit and Multilayer Perceptron (GRU-MLP) ModelXianlin Ma0Mengyao Hou1Jie Zhan2Rong Zhong3College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaCollege of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaCollege of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaCollege of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaShale gas has revolutionized the global energy supply, underscoring the importance of robust production forecasting for the effective management of well operations and gas field development. Nonetheless, the intricate and nonlinear relationship between gas production dynamics and physical constraints like shale formation properties and engineering parameters poses significant challenges. This investigation introduces a hybrid neural network model, GRU-MLP, to proficiently predict shale gas production. The GRU-MLP architecture can capture sequential dependencies within production data as well as the intricate nonlinear correlations between production and the governing constraints. The proposed model was evaluated employing production data extracted from two adjacent horizontal wells situated within the Marcellus Shale. The comparative analysis highlights the superior performance of the GRU-MLP model over the LSTM and GRU models in both short-term and long-term forecasting. Specifically, the GRU model’s mean absolute percentage error of 4.7% and root mean squared error of 120.03 are notably 66% and 80% larger than the GRU-MLP model’s performance in short-term forecasting. The accuracy and reliability of the GRU-MLP model make it a promising tool for shale gas production forecasting. By providing dependable production forecasts, the GRU-MLP model serves to enhance decision-making and optimize well operations.https://www.mdpi.com/2076-3417/13/17/9827GRUhybrid neural networkLSTMshale gaswell production |
| spellingShingle | Xianlin Ma Mengyao Hou Jie Zhan Rong Zhong Enhancing Production Prediction in Shale Gas Reservoirs Using a Hybrid Gated Recurrent Unit and Multilayer Perceptron (GRU-MLP) Model Applied Sciences GRU hybrid neural network LSTM shale gas well production |
| title | Enhancing Production Prediction in Shale Gas Reservoirs Using a Hybrid Gated Recurrent Unit and Multilayer Perceptron (GRU-MLP) Model |
| title_full | Enhancing Production Prediction in Shale Gas Reservoirs Using a Hybrid Gated Recurrent Unit and Multilayer Perceptron (GRU-MLP) Model |
| title_fullStr | Enhancing Production Prediction in Shale Gas Reservoirs Using a Hybrid Gated Recurrent Unit and Multilayer Perceptron (GRU-MLP) Model |
| title_full_unstemmed | Enhancing Production Prediction in Shale Gas Reservoirs Using a Hybrid Gated Recurrent Unit and Multilayer Perceptron (GRU-MLP) Model |
| title_short | Enhancing Production Prediction in Shale Gas Reservoirs Using a Hybrid Gated Recurrent Unit and Multilayer Perceptron (GRU-MLP) Model |
| title_sort | enhancing production prediction in shale gas reservoirs using a hybrid gated recurrent unit and multilayer perceptron gru mlp model |
| topic | GRU hybrid neural network LSTM shale gas well production |
| url | https://www.mdpi.com/2076-3417/13/17/9827 |
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