Production forecasting for normal pressure shale gas wells based on coupling of production decline method and LSTM model
In order to address the challenges posed by the unclear production decline patterns and the difficulty in predicting production for normal pressure shale gas wells, a novel production prediction approach has been developed. This approach combines shale gas well production decline models with Long Sh...
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Editorial Department of Petroleum Reservoir Evaluation and Development
2023-10-01
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| Series: | Youqicang pingjia yu kaifa |
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| Online Access: | https://red.magtech.org.cn/fileup/2095-1426/PDF/1698828997802-1062176569.pdf |
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| author | HAN Kening, WANG Wei, FAN Dongyan, YAO Jun, LUO Fei, YANG Can |
| author_facet | HAN Kening, WANG Wei, FAN Dongyan, YAO Jun, LUO Fei, YANG Can |
| author_sort | HAN Kening, WANG Wei, FAN Dongyan, YAO Jun, LUO Fei, YANG Can |
| collection | DOAJ |
| description | In order to address the challenges posed by the unclear production decline patterns and the difficulty in predicting production for normal pressure shale gas wells, a novel production prediction approach has been developed. This approach combines shale gas well production decline models with Long Short-Term Memory（LSTM） neural network models, leveraging machine learning techniques and different decline models for improved accuracy. Firstly, Nanchuan shale gas wells are divided into two types according to the characteristics of water production. For type 1, gas and water are produced simultaneously at the early stage, then water production decreases significantly in the later stage; while for type 2, gas and water are produced simultaneously for a long time. Secondly, double logarithmic diagnostic curves and characteristic curves are used to identify the flow stages of gas wells; then seven gas production decline models are used to analyze the production variety. Finally, the error of the decline models are used as the inputs of the LSTM model, meanwhile the yield prediction under the coupling method is obtained after superposition. The results show that a type 1 gas well, Well-X1, is in the pesudo-steady flow stage, its optimal decline model is the improved hyperbolic decline model or the AKB model; a type 2 gas well, Well-X2, is in the linear flow stage, the preferred models are SEPD decline and Duong decline model. When the error of the decline model is large, the production prediction accuracy of shale gas wells is effectively improved after coupling the LSTM model but the effect is not obvious when the error of the decline model is small. |
| format | Article |
| id | doaj-art-26efa7066f284779bf8f82cee39325e7 |
| institution | OA Journals |
| issn | 2095-1426 |
| language | zho |
| publishDate | 2023-10-01 |
| publisher | Editorial Department of Petroleum Reservoir Evaluation and Development |
| record_format | Article |
| series | Youqicang pingjia yu kaifa |
| spelling | doaj-art-26efa7066f284779bf8f82cee39325e72025-08-20T02:03:08ZzhoEditorial Department of Petroleum Reservoir Evaluation and DevelopmentYouqicang pingjia yu kaifa2095-14262023-10-0113564765610.13809/j.cnki.cn32-1825/te.2023.05.012Production forecasting for normal pressure shale gas wells based on coupling of production decline method and LSTM modelHAN Kening, WANG Wei, FAN Dongyan, YAO Jun, LUO Fei, YANG Can01. Sinopec East China Oil & Gas Company, Nanjing, Jiangsu 210000, China;2. College of Petroleum Engineering, China University of Petroleum, Qingdao, Shandong 266580, ChinaIn order to address the challenges posed by the unclear production decline patterns and the difficulty in predicting production for normal pressure shale gas wells, a novel production prediction approach has been developed. This approach combines shale gas well production decline models with Long Short-Term Memory（LSTM） neural network models, leveraging machine learning techniques and different decline models for improved accuracy. Firstly, Nanchuan shale gas wells are divided into two types according to the characteristics of water production. For type 1, gas and water are produced simultaneously at the early stage, then water production decreases significantly in the later stage; while for type 2, gas and water are produced simultaneously for a long time. Secondly, double logarithmic diagnostic curves and characteristic curves are used to identify the flow stages of gas wells; then seven gas production decline models are used to analyze the production variety. Finally, the error of the decline models are used as the inputs of the LSTM model, meanwhile the yield prediction under the coupling method is obtained after superposition. The results show that a type 1 gas well, Well-X1, is in the pesudo-steady flow stage, its optimal decline model is the improved hyperbolic decline model or the AKB model; a type 2 gas well, Well-X2, is in the linear flow stage, the preferred models are SEPD decline and Duong decline model. When the error of the decline model is large, the production prediction accuracy of shale gas wells is effectively improved after coupling the LSTM model but the effect is not obvious when the error of the decline model is small.https://red.magtech.org.cn/fileup/2095-1426/PDF/1698828997802-1062176569.pdf|normal shale gas reservoir|production decline method|lstm model|flow stage|double logarithmic diagnosis|coupling method |
| spellingShingle | HAN Kening, WANG Wei, FAN Dongyan, YAO Jun, LUO Fei, YANG Can Production forecasting for normal pressure shale gas wells based on coupling of production decline method and LSTM model Youqicang pingjia yu kaifa |normal shale gas reservoir|production decline method|lstm model|flow stage|double logarithmic diagnosis|coupling method |
| title | Production forecasting for normal pressure shale gas wells based on coupling of production decline method and LSTM model |
| title_full | Production forecasting for normal pressure shale gas wells based on coupling of production decline method and LSTM model |
| title_fullStr | Production forecasting for normal pressure shale gas wells based on coupling of production decline method and LSTM model |
| title_full_unstemmed | Production forecasting for normal pressure shale gas wells based on coupling of production decline method and LSTM model |
| title_short | Production forecasting for normal pressure shale gas wells based on coupling of production decline method and LSTM model |
| title_sort | production forecasting for normal pressure shale gas wells based on coupling of production decline method and lstm model |
| topic | |normal shale gas reservoir|production decline method|lstm model|flow stage|double logarithmic diagnosis|coupling method |
| url | https://red.magtech.org.cn/fileup/2095-1426/PDF/1698828997802-1062176569.pdf |
| work_keys_str_mv | AT hankeningwangweifandongyanyaojunluofeiyangcan productionforecastingfornormalpressureshalegaswellsbasedoncouplingofproductiondeclinemethodandlstmmodel |