Fluid Flow Velocity Measurement in Active Wells Using Fiber Optic Distributed Acoustic Sensors

Vahabi, Nafiseh; Willman, Eero; Baghsiahi, H; Selviah, David R.

doi:10.1109/jsen.2020.2996823

Cited by 15 publications

(13 citation statements)

References 31 publications

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“…Several different approaches can be used to measure the multiphase flow, including but not limited to, conventional dedicated hardware-based flow meters [ 7 , 10 , 14 , 15 , 43 ], virtual flow meters [ 17 , 18 , 39 , 42 , 45 , 46 , 47 ], and distributed sensor flow estimations [ 8 , 37 , 40 , 48 ]. This paper focuses on providing a comprehensive review of the last approach, using distributed sensors with physical flow modelling and machine learning algorithms for multiphase flow estimation.…”

Section: Multiphase Fluid Flowmentioning

confidence: 99%

“…After dividing the data into several blocks, it is often required to transform each blocks of DAS data from time-space domain, corresponding to the time of data being recorded and the spatial locations within the fibre cable, to the frequency and wavenumber domain, also called F-K domain. The two dimensional Fast Fourier Transforms (FFT) algorithm [ 40 ] can be used to perform such transformation (

) as depicted in Figure 6 . It is defined in Equation ( 1 ) where

denote the time and location of the input data, while

denote the frequency and wavenumber, respectively.…”

Section: Physical Flow Modellingmentioning

confidence: 99%

“…Another interesting work was presented by Vahabi et al [ 37 , 40 ]. The DAS data, collected from real oil, water and gas well pipes under the sea, was used to identify fluid types [ 37 ] and to estimate fluid flow velocities [ 40 ] using machine learning algorithms. The F-K transformation technique was employed to provide input data for the CNN and ANN models to classify the type of fluid in pipes.…”

Section: Machine Learningmentioning

confidence: 99%

“…Other machine learning algorithms, such as Cross-correlation, K-Means, and Radial Integration, were also employed to determine fluid flow velocity in pipes. Flow velocity from the wellhead was used as the true label, and with some physical assumptions, the machine learning algorithms performed quite well on estimating the flow velocity from input data derived according to the F-K transformation [ 40 ].…”

Section: Machine Learningmentioning

confidence: 99%

“…For example, Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) have been used to perform flow regime classification and multiphase estimation [ 36 , 37 ]. Subsequently, the ANN with Long Short-Term Memory (LSTM) algorithm has also been used to perform fluid flow rate estimation [ 38 , 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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.

show abstract

Section: Multiphase Fluid Flowmentioning

confidence: 99%

) as depicted in Figure 6 . It is defined in Equation ( 1 ) where

denote the time and location of the input data, while

denote the frequency and wavenumber, respectively.…”

Section: Physical Flow Modellingmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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An indirect wireless electromagnetic measurement of flow velocity by an SRR‐based sensing method

Liu

Lin

et al. 2021

Micro & Optical Tech Letters

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This study describes an indirect wireless electromagnetic measurement of flow velocity by measuring the state of the SRR in the waveguide. When the fluid exhibits poor sensitivity to electromagnetic wave, the inclination angle between the SRR and the side wall of the waveguide becomes a direct reflection of flow velocity and affects the resonance frequency of the SRR. To be specific, different inclination angles will cause a variable stopband in the passband of the waveguide. By measuring the inclination angle-sensitive frequency response of the SRR in the waveguide, the flow velocity can be measured indirectly. Based on this principle, an experiment using WR-187 rectangular waveguide is produced. A new C-type SRR is designed and placed in the waveguide with variable inclination angles. As revealed from the simulation and experimental results, when the inclination angle varies from 0 to 90 , the resonance frequency of the SRR shifts from 4.145 to 4.226 GHz. On that basis, the flow velocity at one or more places can be obtained.

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Flow characteristics of gas and liquid in pipeline revealed by machine learning on distributed acoustic sensing data

Ku,

Zhang,

et al. 2025

Geoenergy Science and Engineering

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Fluid Flow Velocity Measurement in Active Wells Using Fiber Optic Distributed Acoustic Sensors

Cited by 15 publications

References 31 publications

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

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

An indirect wireless electromagnetic measurement of flow velocity by an SRR‐based sensing method

Flow characteristics of gas and liquid in pipeline revealed by machine learning on distributed acoustic sensing data

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