Search: onr:"swepub:oai:DiVA.org:ltu-103549" >
Deep Learning-Based...
Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data
-
- Cheng, Haibo (author)
- Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, China; Chinese Academy of Sciences, Key Laboratory of Networked Control Systems, Shenyang, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
-
- He, Yunpeng (author)
- Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, China; Chinese Academy of Sciences, Key Laboratory of Networked Control Systems, Shenyang, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China; University of Chinese Academy of Sciences, Beijing, China
-
- Zeng, Peng (author)
- Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, China; Chinese Academy of Sciences, Key Laboratory of Networked Control Systems, Shenyang, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
-
show more...
-
- Li, Shichao (author)
- Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, China; Chinese Academy of Sciences, Key Laboratory of Networked Control Systems, Shenyang, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
-
- Vyatkin, Valeriy (author)
- Luleå tekniska universitet,Datavetenskap,Aalto University, Department of Electrical Engineering and Automation, Helsinki, Finland
-
show less...
-
(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- English.
-
In: IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. - : Institute of Electrical and Electronics Engineers (IEEE).
- Related links:
-
https://urn.kb.se/re...
-
show more...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction.
Subject headings
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Geoteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Geotechnical Engineering (hsv//eng)
Keyword
- deep learning
- spatio-temporal data
- stacked gate recurrent unit network
- subsurface oil reservoir pressure
- Dependable Communication and Computation Systems
- Kommunikations- och beräkningssystem
Publication and Content Type
- ref (subject category)
- kon (subject category)
To the university's database