Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network
A well control optimization method is a key technology to adjust the flow direction of waterflooding and improve the effect of oilfield development. The existing well control optimization method is mainly based on optimization algorithms and numerical simulators. In the face of larger models, longer...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2021/8873782 |
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| _version_ | 1832568542445174784 |
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| author | Lihui Tang Junjian Li Wenming Lu Peiqing Lian Hao Wang Hanqiao Jiang Fulong Wang Hongge Jia |
| author_facet | Lihui Tang Junjian Li Wenming Lu Peiqing Lian Hao Wang Hanqiao Jiang Fulong Wang Hongge Jia |
| author_sort | Lihui Tang |
| collection | DOAJ |
| description | A well control optimization method is a key technology to adjust the flow direction of waterflooding and improve the effect of oilfield development. The existing well control optimization method is mainly based on optimization algorithms and numerical simulators. In the face of larger models, longer optimization periods, or reservoir models with a large number of optimized wells, there are many optimization variables, which will cause algorithm convergence difficulties and optimization costs. The application effect is not good because of the problems of time length, few comparison schemes, and only fixed control frequency. This paper proposes a new method of a well control optimization method based on a multi-input deep neural network. This method takes the production history data of the reservoir as the main input and the saturation field as the auxiliary input and establishes a multi-input deep neural network for learning, forming a production dynamic prediction model instead of conventional numerical simulators. Based on the production dynamic prediction model, a series of model generation, production prediction, comparison, and optimization are carried out to find the best production plan of the reservoir. The calculation results of the examples show that (1) compared with the single-input production dynamic prediction model, the production dynamic prediction model based on multiple inputs has better prediction accuracy, and the results are close to the calculation results of the conventional numerical simulator; (2) the well control optimization method based on the multiple-input deep neural network has a fast optimization speed, with many comparison schemes and good optimization effect. |
| format | Article |
| id | doaj-art-fe6c59d3be3b4c21a3eed13994534ce3 |
| institution | Kabale University |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-fe6c59d3be3b4c21a3eed13994534ce32025-02-03T00:58:50ZengWileyGeofluids1468-81151468-81232021-01-01202110.1155/2021/88737828873782Well Control Optimization of Waterflooding Oilfield Based on Deep Neural NetworkLihui Tang0Junjian Li1Wenming Lu2Peiqing Lian3Hao Wang4Hanqiao Jiang5Fulong Wang6Hongge Jia7College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaCollege of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaSINOPEC Petroleum Exploration and Production Research Institute, Beijing 100083, ChinaSINOPEC Petroleum Exploration and Production Research Institute, Beijing 100083, ChinaCollege of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaCollege of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaCollege of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaAktobe Corporation, PetroChina International (Kazakhstan), Beijing 100011, ChinaA well control optimization method is a key technology to adjust the flow direction of waterflooding and improve the effect of oilfield development. The existing well control optimization method is mainly based on optimization algorithms and numerical simulators. In the face of larger models, longer optimization periods, or reservoir models with a large number of optimized wells, there are many optimization variables, which will cause algorithm convergence difficulties and optimization costs. The application effect is not good because of the problems of time length, few comparison schemes, and only fixed control frequency. This paper proposes a new method of a well control optimization method based on a multi-input deep neural network. This method takes the production history data of the reservoir as the main input and the saturation field as the auxiliary input and establishes a multi-input deep neural network for learning, forming a production dynamic prediction model instead of conventional numerical simulators. Based on the production dynamic prediction model, a series of model generation, production prediction, comparison, and optimization are carried out to find the best production plan of the reservoir. The calculation results of the examples show that (1) compared with the single-input production dynamic prediction model, the production dynamic prediction model based on multiple inputs has better prediction accuracy, and the results are close to the calculation results of the conventional numerical simulator; (2) the well control optimization method based on the multiple-input deep neural network has a fast optimization speed, with many comparison schemes and good optimization effect.http://dx.doi.org/10.1155/2021/8873782 |
| spellingShingle | Lihui Tang Junjian Li Wenming Lu Peiqing Lian Hao Wang Hanqiao Jiang Fulong Wang Hongge Jia Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network Geofluids |
| title | Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network |
| title_full | Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network |
| title_fullStr | Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network |
| title_full_unstemmed | Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network |
| title_short | Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network |
| title_sort | well control optimization of waterflooding oilfield based on deep neural network |
| url | http://dx.doi.org/10.1155/2021/8873782 |
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