dc.contributor.author | Arief, Hasan Asyari | |
dc.contributor.author | Wiktorski, Tomasz | |
dc.contributor.author | Thomas, Peter | |
dc.date.accessioned | 2023-02-17T12:54:52Z | |
dc.date.available | 2023-02-17T12:54:52Z | |
dc.date.created | 2021-05-01T17:02:55Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Arief, H. A. A., Wiktorski, T., & Thomas, P. J. (2021). A survey on distributed fibre optic sensor data modelling techniques and machine learning algorithms for multiphase fluid flow estimation. Sensors, 21(8), 2801. | en_US |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | https://hdl.handle.net/11250/3051977 | |
dc.description.abstract | Real-time monitoring of multiphase fluid flows with distributed fibre optic sensing has the potential to play a major role in industrial flow measurement applications. One such application is the optimization of hydrocarbon production to maximize short-term income, and prolong the operational lifetime of production wells and the reservoir. While the measurement technology itself is well understood and developed, a key remaining challenge is the establishment of robust data analysis tools that are capable of providing real-time conversion of enormous data quantities into actionable process indicators. This paper provides a comprehensive technical review of the data analysis techniques for distributed fibre optic technologies, with a particular focus on characterizing fluid flow in pipes. The review encompasses classical methods, such as the speed of sound estimation and Joule-Thomson coefficient, as well as their data-driven machine learning counterparts, such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Ensemble Kalman Filter (EnKF) algorithms. The study aims to help end-users establish reliable, robust, and accurate solutions that can be deployed in a timely and effective way, and pave the wave for future developments in the field. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | The authors | en_US |
dc.subject.nsi | VDP::Teknologi: 500 | en_US |
dc.source.journal | Sensors | en_US |
dc.identifier.doi | 10.3390/s21082801 | |
dc.identifier.cristin | 1907603 | |
dc.relation.project | Norges forskningsråd: 308840 | en_US |
dc.relation.project | Notur/NorStore: NN9856K | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |