ReviewSensors (Basel, Switzerland)2021
A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
7 citing papers in PubMed, 47 citations in OpenAlex.
- The Application of Kernel Ridge Regression for the Improvement of a Sensing Interferometric System.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning Applications in Optical Fiber Sensing: A Research Agenda.Sensors (Basel, Switzerland) · 2024Review
- Deep Learning for Optical Sensor Applications: A Review.Sensors (Basel, Switzerland) · 2023Review
- Automated Damage Detection Using Lamb Wave-Based Phase-Sensitive OTDR and Support Vector Machines.Sensors (Basel, Switzerland) · 2023Article
- Towards Building a Distributed Virtual Flow Meter via Compressed Continual Learning.Sensors (Basel, Switzerland) · 2022Article
- 2D Temperature Field Reconstruction Using Optical Frequency Domain Reflectometry and Machine-Learning Algorithms.Sensors (Basel, Switzerland) · 2022Article
- Distributed Acoustic Sensing (DAS) Response of Rising Taylor Bubbles in Slug Flow.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.