Abstract
In cyber-physical petroleum systems (CPPS), accurate estimation of interwell connectivity is an important process to know reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas (OG) field. In this study, an artificial neural network (ANN) based analysis method is proposed to estimate interwell connectivity. The generated neural network is used to define the mapping function between production wells and surrounding injection wells based on the historical water injection and liquid production data. Finally, the proposed method is applied to a synthetic reservoir model. Experimental results show that ANN based approach is an efficient method for analyzing interwell connectivity.
Original language | English |
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Title of host publication | Proceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019 |
Publisher | IEEE |
Pages | 199-205 |
Number of pages | 7 |
ISBN (Electronic) | 9781728129273 |
DOIs | |
Publication status | Published - 1 Jul 2019 |
MoE publication type | A4 Conference publication |
Event | IEEE International Conference on Industrial Informatics - Aalto University, Helsinki and Espoo, Finland Duration: 22 Jul 2019 → 25 Jul 2019 Conference number: 17 https://www.indin2019.org/ |
Publication series
Name | IEEE International Conference on Industrial Informatics |
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Publisher | IEEE |
ISSN (Print) | 1935-4576 |
ISSN (Electronic) | 2378-363X |
Conference
Conference | IEEE International Conference on Industrial Informatics |
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Abbreviated title | INDIN |
Country/Territory | Finland |
City | Helsinki and Espoo |
Period | 22/07/2019 → 25/07/2019 |
Internet address |
Keywords
- Artificial neural network (ANN)
- Cyber-physical petroleum systems(CPPS)
- Interwell connectivity
- Long short-term memory (LSTM)
- Waterflooded reservoir