Time series forecasting of oil production in Enhanced Oil Recovery system based on a novel CNN-GRU neural network
文献类型：期刊论文
| 作者 | Chen, Guangxu3,4; Tian, Hailong2,3,4; Xiao, Ting1,5; Xu, Tianfu3,4; Lei, Hongwu6 |
| 刊名 | GEOENERGY SCIENCE AND ENGINEERING
 |
| 出版日期 | 2024-02-01 |
| 卷号 | 233页码:12 |
| 关键词 | Oil production forecasting Deep learning Convolutional neural network Gate recurrent unit EOR Bayesian optimization algorithm |
| ISSN号 | 2949-8929 |
| DOI | 10.1016/j.geoen.2023.212528 |
| 英文摘要 | An accurate prediction of oil production is critical for the oilfield development, and many deep learning models have been widely employed for this purpose. However, those methods show insufficiencies in extracting complex features from multivariable time series datasets, which leaves the prediction of oil production still challenging. In this study, a novel CNN-GRU model combining Convolutional Neural Networks (CNN) and Gate Recurrent Unit (GRU) neural network was proposed to accurately predict oil production for Enhanced Oil Recovery (EOR) performance. The CNN layer can extract the features from variables affecting oil production, and the GRU layer models temporal information using the transmitted features for prediction. The Bayesian Optimization algorithm (BO) was employed to design the optimal hyper-parameters of CNN-GRU. For evaluation purpose, two case studies were carried out with the production data from a CO2-EOR project and a waterflooding project. The prediction performance of the proposed approach was compared with typical deep learning methods and a hybrid (statistical and machine learning) method. The results of experiments and comparisons indicate that the proposed CNN-GRU model outperforms other prediction approaches. The CNN-GRU model provides future oil production of wells, enabling engineers to make informed decisions in development plan of reservoirs. |
| 资助项目 | National Natural Science Foundation of China[42141013] ; National Natural Science Foundation of China[41772247] ; CNPC Innovation Found[2021 D002-1102] |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001132231800001 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/40170]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Tian, Hailong |
| 作者单位 | 1.Univ Utah, Energy & Geosci Inst, Salt Lake City, UT 84108 USA 2.Jilin Univ, Key Lab Groundwater Resources & Environm, Minist Educ, Changchun 130021, Peoples R China 3.Jilin Univ, Key Lab Groundwater Resources & Environm, Minist Educ, Changchun 130021, Peoples R China 4.Jilin Univ, Jilin Prov Key Lab Water Resources & Environm, Changchun 130021, Peoples R China 5.Univ Utah, Dept Civil & Environm Engn, Salt Lake City, UT USA 6.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Guangxu,Tian, Hailong,Xiao, Ting,et al. Time series forecasting of oil production in Enhanced Oil Recovery system based on a novel CNN-GRU neural network[J]. GEOENERGY SCIENCE AND ENGINEERING,2024,233:12. |
| APA | Chen, Guangxu,Tian, Hailong,Xiao, Ting,Xu, Tianfu,&Lei, Hongwu.(2024).Time series forecasting of oil production in Enhanced Oil Recovery system based on a novel CNN-GRU neural network.GEOENERGY SCIENCE AND ENGINEERING,233,12. |
| MLA | Chen, Guangxu,et al."Time series forecasting of oil production in Enhanced Oil Recovery system based on a novel CNN-GRU neural network".GEOENERGY SCIENCE AND ENGINEERING 233(2024):12. |
入库方式： OAI收割
来源：武汉岩土力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。