The Use of Multi-frequency Acquisition to Significantly Improve the Quality of Fibre-optic Distributed Vibration Sensing

Hartog, Arthur H.; Лиокумович, Л. Б.; Ushakov, Nikolai; Котов, О. И.; Dean, Tim; Cuny, T.; Constantinou, Alexis

doi:10.3997/2214-4609.201600685

Cited by 16 publications

(11 citation statements)

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“…The main benefit of the approach implemented in hDVS is that the phase is digitally acquired at the beat frequency [22]. This allows us to perform the differentiation in the digital domain, and therefore to vary its parameters as desired as a way to control the sensitivity and spatial resolution of the system.…”

Section: A Distributed Sensing With Rayleigh Backscattermentioning

confidence: 99%

“…This allows us to perform the differentiation in the digital domain, and therefore to vary its parameters as desired as a way to control the sensitivity and spatial resolution of the system. The hDVS interrogator also implements a multi-frequency approach, where multiple beat frequencies are used simultaneously to interrogate the fiber, and combined during processing to help eliminate the fading, which is the natural phenomenon where the response of the scatters cancels and no light is being backscattered in a section of the fiber [22].…”

Section: A Distributed Sensing With Rayleigh Backscattermentioning

confidence: 99%

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Low-Frequency Distributed Acoustic Sensing for Early Gas Detection in a Wellbore

et al. 2021

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An unexpected and unwanted influx of gas or "kick" into the wellbore during hydrocarbon drilling can cause catastrophic blowout incidents, resulting in human casualties, ecological damage, and asset losses. The ability of the oil and gas industry to control gas kick depends on our ability to accurately detect and monitor gas migration in a borehole in real-time. This study demonstrates the application of optical fiber-based Distributed Acoustic Sensors (DAS) for early detection and monitoring of gas in wellbore. Multiphase flow experiments conducted in a 5000 ft. deep test-well are analyzed for different injection, circulation, and pressure conditions. In each case, the low-frequency component of DAS demonstrates a superior capability to detect gas signatures both inside the tubing and the annulus of the well, even at small gas volumes. In comparison, the highfrequency DAS data seems limited in detail. The gas influx velocity was calculated using the frequency-wavenumber analysis of the gradient of the low-frequency DAS phase with respect to time, which shows good agreement with theoretical velocity estimates using flow models and surface gauge measurements. This study demonstrates a novel workflow to analyze low-frequency DAS to qualitatively and quantitatively map gas influx in a wellbore.

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Section: A Distributed Sensing With Rayleigh Backscattermentioning

confidence: 99%

Section: A Distributed Sensing With Rayleigh Backscattermentioning

confidence: 99%

Low-Frequency Distributed Acoustic Sensing for Early Gas Detection in a Wellbore

et al. 2021

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“…Various approaches can be used to deal with the diversity results to reduce fading probability, such as optimum selection, direct averaging, or weighted averaging method. In Reference [19], multi-frequency diversity is utilized and it has been further proved that the aggregation of the frequency diversity results can also help to reduce the overall noise of the measurement and improve the linearity of Φ-OTDR. Among different aggregation methods, the rotated-vector-sum is a kind of weighted averaging method which uses the signal amplitude of individual frequency as the weight information to identify the phase quality, and turns out to be more efficient [19], [20].…”

Section: Introductionmentioning

confidence: 99%

“…In Reference [19], multi-frequency diversity is utilized and it has been further proved that the aggregation of the frequency diversity results can also help to reduce the overall noise of the measurement and improve the linearity of Φ-OTDR. Among different aggregation methods, the rotated-vector-sum is a kind of weighted averaging method which uses the signal amplitude of individual frequency as the weight information to identify the phase quality, and turns out to be more efficient [19], [20]. In theory, the more the number of frequencies, the bigger the SNR can be improved, which has the potential to address the SNR limitation caused by the weak RBS coefficient.…”

Section: Introductionmentioning

confidence: 99%

High SNR Φ-OTDR Based on Frequency and Wavelength Diversity With Differential Vector Aggregation Method

Lü

Yang

et al. 2020

IEEE Photonics J.

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Phase-demodulated phase-sensitive time domain reflectometer (Φ-OTDR) has attracted much attention because of its linear relationship between the phase variation and the amplitude of the vibration. However, it suffers from limited signal-to-noise ratio (SNR) and signal fading, due to the inherent low Rayleigh scattering coefficient and randomly spatial non-uniform distribution of the refractive index. Here, a Φ-OTDR system based on wavelength diversity is demonstrated, which can enhance the SNR and reduce signal fading simultaneously by using differential vector aggregation method. A theoretical model is built, and the linear relationship between SNR improvement and diversity scale is analyzed. The verification experiment is conducted, and in the further experiments, the frequency and wavelength diversity technology are cascaded to achieve a diversity scale of 20. After differential vector aggregation method, the external disturbance signal is well reconstructed and the noise floor is reduced by 13.4dB, which is basically consistent with the theoretical analysis.

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“…Other systems, like Fibre Bragg Grating technology [5] based systems, are limited in frequency range, which makes it impossible to detect certain types of damages. Newly improved systems can overcome this obstacle, but these systems are expensive, mostly due to complicated but high quality equipment [6]. From this point of view, vibration based methods have considerable potential for further development [7], especially those methods that utilize modal analysis [8].…”

Section: Introductionmentioning

confidence: 99%

Application case: prototype of radar tower structural health monitoring system

Mironovs

Mironov²,

Chate

2018

Engineering for Rural Development

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Abstract. Industrial and civil structures like bridges, wind turbines, pipelines etc., are widely present in rural area. To maintain functionality of these structures, their condition is controlled using Structural Health Monitoring (SHM). SHM assists engineers in finding early structural defects like cracks, deformations, material deterioration, metal fatigue and so on. Monitoring can prevent accidents from happening by giving early warnings and a chance to perform necessary repairs and adjustments to the structure. It is efficient and economically reasonable to have possibility to perform planned repairs well ahead, instead of doing urgent fixing. What is more important, structural condition monitoring improves safety along with reducing maintenance costs. There are numerous non-destructive techniques (ultrasound, Fiber Bragg Grating, acoustic emission) applied for SHM, but the focus of this paper is on vibration-based methods, particularly -Operational Modal Analysis (OMA). The aim of the study is to validate SHM application possibility for a full scale industrial structure using the Operational Modal Analysis based SHM system. The system is designed as a cost-effective solution, mainly by using cheap piezo film deformation sensors and utilizing multi-patch OMA technique. A test was performed and modal parameters of the radar tower were obtained and compared with the FE model. The possibility to perform SHM on an industrial structure was validated.

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The Use of Multi-frequency Acquisition to Significantly Improve the Quality of Fibre-optic Distributed Vibration Sensing

Cited by 16 publications

References 0 publications

Low-Frequency Distributed Acoustic Sensing for Early Gas Detection in a Wellbore

Low-Frequency Distributed Acoustic Sensing for Early Gas Detection in a Wellbore

High SNR Φ-OTDR Based on Frequency and Wavelength Diversity With Differential Vector Aggregation Method

Application case: prototype of radar tower structural health monitoring system

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