Prediction of Oil Saturation during Water and Gas Injection Using Controllable Convolutional Long Short-Term Memory
Oil saturation is a kind of spatiotemporal sequence that changes dynamically with time, and it is affected not only by the reservoir properties, but also by the injection–production parameters. When predicting oil saturation during water and gas injection, the influence of time, space and injection–...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/14/5063 |
| _version_ | 1797406591710920704 |
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| author | Yukun Dong Fubin Liu Yu Zhang Qiong Wu |
| author_facet | Yukun Dong Fubin Liu Yu Zhang Qiong Wu |
| author_sort | Yukun Dong |
| collection | DOAJ |
| description | Oil saturation is a kind of spatiotemporal sequence that changes dynamically with time, and it is affected not only by the reservoir properties, but also by the injection–production parameters. When predicting oil saturation during water and gas injection, the influence of time, space and injection–production parameters should be considered. Aiming at this issue, a prediction method based on a controllable convolutional long short-term memory network (Ctrl-CLSTM) is proposed in this paper. The Ctrl-CLSTM is an unsupervised learning model whose input is the previous spatiotemporal sequence together with the controllable factors of corresponding moments, and the output is the sequence to be predicted. In this way, future oil saturation can be generated from the historical context. Concretely, the convolution operation is embedded into each unit to describe the interaction between temporal features and spatial structures of oil saturation, thus the Ctrl-CLSTM realizes the unified modeling of the spatiotemporal features of oil saturation. In addition, a novel control gate structure is introduced in each Ctrl-CLSTM unit to take the injection–production parameters as controllable influencing factors and establish the nonlinear relationship between oil saturation and injection–production parameters according to the coordinates of each well location. Therefore, different oil saturation prediction results can be obtained by changing the injection–production parameters. Finally, experiments on real oilfields show that the Ctrl-CLSTM comprehensively considers the influence of artificial controllable factors such as injection–production parameters, accomplishes accurate prediction of oil saturation with a structure similarity of more than 98% and is more time efficient than reservoir numerical simulation. |
| first_indexed | 2024-03-09T03:28:39Z |
| format | Article |
| id | doaj.art-a778220d9a7446ffa5bb1af1c8009764 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T03:28:39Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a778220d9a7446ffa5bb1af1c80097642023-12-03T14:58:42ZengMDPI AGEnergies1996-10732022-07-011514506310.3390/en15145063Prediction of Oil Saturation during Water and Gas Injection Using Controllable Convolutional Long Short-Term MemoryYukun Dong0Fubin Liu1Yu Zhang2Qiong Wu3College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaCollege of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaCollege of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaResearch Institute of Exploration and Development, Tarim Oilfield Company, PetroChina, Korla 841000, ChinaOil saturation is a kind of spatiotemporal sequence that changes dynamically with time, and it is affected not only by the reservoir properties, but also by the injection–production parameters. When predicting oil saturation during water and gas injection, the influence of time, space and injection–production parameters should be considered. Aiming at this issue, a prediction method based on a controllable convolutional long short-term memory network (Ctrl-CLSTM) is proposed in this paper. The Ctrl-CLSTM is an unsupervised learning model whose input is the previous spatiotemporal sequence together with the controllable factors of corresponding moments, and the output is the sequence to be predicted. In this way, future oil saturation can be generated from the historical context. Concretely, the convolution operation is embedded into each unit to describe the interaction between temporal features and spatial structures of oil saturation, thus the Ctrl-CLSTM realizes the unified modeling of the spatiotemporal features of oil saturation. In addition, a novel control gate structure is introduced in each Ctrl-CLSTM unit to take the injection–production parameters as controllable influencing factors and establish the nonlinear relationship between oil saturation and injection–production parameters according to the coordinates of each well location. Therefore, different oil saturation prediction results can be obtained by changing the injection–production parameters. Finally, experiments on real oilfields show that the Ctrl-CLSTM comprehensively considers the influence of artificial controllable factors such as injection–production parameters, accomplishes accurate prediction of oil saturation with a structure similarity of more than 98% and is more time efficient than reservoir numerical simulation.https://www.mdpi.com/1996-1073/15/14/5063oil saturationspatiotemporal sequenceoil recoverycontrollable convolutional LSTM |
| spellingShingle | Yukun Dong Fubin Liu Yu Zhang Qiong Wu Prediction of Oil Saturation during Water and Gas Injection Using Controllable Convolutional Long Short-Term Memory Energies oil saturation spatiotemporal sequence oil recovery controllable convolutional LSTM |
| title | Prediction of Oil Saturation during Water and Gas Injection Using Controllable Convolutional Long Short-Term Memory |
| title_full | Prediction of Oil Saturation during Water and Gas Injection Using Controllable Convolutional Long Short-Term Memory |
| title_fullStr | Prediction of Oil Saturation during Water and Gas Injection Using Controllable Convolutional Long Short-Term Memory |
| title_full_unstemmed | Prediction of Oil Saturation during Water and Gas Injection Using Controllable Convolutional Long Short-Term Memory |
| title_short | Prediction of Oil Saturation during Water and Gas Injection Using Controllable Convolutional Long Short-Term Memory |
| title_sort | prediction of oil saturation during water and gas injection using controllable convolutional long short term memory |
| topic | oil saturation spatiotemporal sequence oil recovery controllable convolutional LSTM |
| url | https://www.mdpi.com/1996-1073/15/14/5063 |
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