GPU-based fast processing for a distributed acoustic sensor using an LFM
				pulse

Wang, Shuanghao; Jiang, Junfeng; Wang, Shuang; Ma, Zhe; Xu, Tianhua; Ding, Zhenyang; Lv, Zhankun

doi:10.1364/ao.412184

Cited by 6 publications

(1 citation statement)

References 22 publications

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“…Several techniques can be used to speed up the data preprocessing step. For example, GPU-based implementation can be used for preprocessing the DAS data [100]. Generative Adversarial Network (GAN) can be used to simplify the data generation process which can help reduce the total processing time [101].…”

Section: Data Preprocessingmentioning

confidence: 99%

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

Arief

Wiktorski

Thomas

2021

Sensors

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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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Section: Data Preprocessingmentioning

confidence: 99%