Otchere, D.A. and Latiff, A.H. and Tackie-Otoo, B.N. (2024) Distributed acoustic sensing in subsurface applications � Review and potential integration with artificial intelligence for an intelligent CO2 storage monitoring system. Geoenergy Science and Engineering, 237.
Full text not available from this repository.Abstract
Distributed Acoustic Sensing (DAS) technology uses optical fibres to detect and measure vibrations along their length. It has a wide range of applications, including subsurface imaging and reservoir characterisation and monitoring. In the oil and gas industry, DAS can be used to monitor the health of reservoirs, identify the location and movement of fluids, and track the effectiveness of production and injection processes. One potential application of DAS in subsurface imaging is to map the distribution of reservoirs and identify the presence of hydrocarbons. This can be done by measuring the acoustic waves generated by the movement of fluids in the subsurface. DAS can also be used to monitor reservoirs in real time, providing information on the location and movement of fluids, as well as the mechanical properties of rock formations. This information is crucial for optimising production and injection processes and predicting the reservoir's future behaviour. There is also potential for DAS data to be processed by artificial intelligence (AI) to create an intelligent monitoring system for CO2 storage. By using machine learning algorithms, it may be possible to analyse the data collected by DAS in real-time and identify patterns that could indicate potential problems with the CO2 storage process. This could allow for timely intervention and prevent costly and potentially dangerous leaks. Overall, the integration of DAS and AI has the potential to revolutionise the way subsurface reservoirs and CO2 storage systems are monitored and managed. © 2024 Elsevier B.V.
Item Type: | Article |
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Additional Information: | cited By 0 |
Uncontrolled Keywords: | Digital storage; Gas industry; Learning algorithms; Machine learning; Optical fibers, Acoustic sensing; CO2 storage; Distributed acoustic sensing; Injection process; Production process; Real- time; Reservoir monitoring; Seismic imaging; Sub-surface imaging; Subsurface reservoir, Carbon dioxide, acoustic wave; algorithm; artificial intelligence; carbon storage; machine learning; monitoring system; seismicity |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 04 Jun 2024 14:19 |
Last Modified: | 04 Jun 2024 14:19 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/19646 |