OptaSense[reg ] Distributed Acoustic Sensing (DAS) System for the Power Network - Integrated SMART-Sensing REAL TIME MONITORING

Author(s):  
K. Singh ◽  
C. Minto ◽  
A. Godfrey
2021 ◽  
Author(s):  
Rajeev Kumar ◽  
Pierre Bettinelli

Abstract During the evolution of the petroleum industry, surface seismic imaging has played a critical role in reservoir characterization. In the early days, borehole seismic (BHS) was developed to complement surface seismic. However, in the last few decades, a wide range of BHS surveys has been introduced to cater to new and unique objectives over the oilfield lifecycle. In the exploration phase, vertical seismic profiling (VSP) provides critical time-depth information to bridge time indexed subsurface images to log/reservoir properties in depth. This information can be obtained using several methods like conventional wireline checkshot or zero-offset vertical seismic profiling (ZVSP), seismic while drilling (SWD) or distributed acoustic sensing (DAS) techniques. SWD is a relatively new technique to record real-time data using tool deployed in the bottomhole assembly without disturbing the drilling. It helps to improve decision making for safer drilling especially in new areas in a cost-effective manner. Recently, a breakthrough technology, distributed acoustic sensing (DAS), has been introduced, where data are recorded using a fiber-optic cable with lots of saving. ZVSP also provides several parameters like, attenuation coefficient (Q), multiples prediction, impedance, reflectivity etc., which helps with characterizing the subsurface and seismic reprocessing. In the appraisal phase, BHS applications vary from velocity model update, anisotropy estimation, well- tie to imaging VSPs. The three-component VSP data is best suited for imaging and amplitude variation with offset (AVO) due to several factors like less noise interference due to quiet downhole environment, higher frequency bandwidth, proximity to the reflector, etc. Different type of VSP surveys (offset, walkaway, walkaround etc.) were designed to fulfill objectives like imaging, AVO, Q, anisotropy, and fracture mapping. In the development phase, high-resolution images (3D VSP, walkaway, or crosswell) from BHS surveys can assist with optimizing the drilling of new wells and, hence reduce costs. it can help with landing point selection, horizontal section placement, and refining interpretation for reserve calculation. BHS offers a wide range of surveys to assist the oilfield lifecycle during the production phase. Microseismic monitoring is an industry-known service to optimize hydraulic fracturing and is the only technique that captures the induced seismicity generated by hydraulic fracturing and estimate the fracture geometry (height, width, and azimuth) and in real time. During enhanced oil recovery (EOR) projects, BHS can be useful to optimize the hydrocarbon drainage strategies by mapping the fluid movement (CO2, water, steam) using time-lapse surveys like walkaway, 3D VSP and/or crosswell. DAS has brought a new dimension to provide vital information on injection or production evaluation, leak detection, flow behind tubing, crossflow diagnosis, and cement evaluation during production phase. This paper highlights the usage of BHS over the lifecycle of the oilfield.


2021 ◽  
Author(s):  
Martijn van den Ende ◽  
André Ferrari ◽  
Anthony Sladen ◽  
Cédric Richard

Distributed Acoustic Sensing (DAS) is a novel vibration sensing technology that can be employed to detect vehicles and to analyse traffic flows using existing telecommunication cables. DAS therefore has great potential in future "smart city" developments, such as real-time traffic incident detection. Though previous studies have considered vehicle detection under relatively light traffic conditions, in order for DAS to be a feasible technology in real-world scenarios, detection algorithms need to also perform robustly under heavy traffic conditions. In this study we investigate the potential of roadside DAS for the simultaneous detection and characterisation of the velocity of individual vehicles. To improve the temporal resolution and detection accuracy, we propose a self-supervised Deep Learning approach that deconvolves the characteristic car impulse response from the DAS data, which we refer to as a Deconvolution Auto-Encoder (DAE). We show that deconvolution of the DAS data with our DAE leads to better temporal resolution and detection performance than the original (non-deconvolved) data. We subsequently apply our DAE to a 24-hour traffic cycle, demonstrating the feasibility of our proposed method to process large volumes of DAS data, potentially in near-real time.


2020 ◽  
Vol 10 (2) ◽  
pp. 448 ◽  
Author(s):  
Christoph Wiesmeyr ◽  
Martin Litzenberger ◽  
Markus Waser ◽  
Adam Papp ◽  
Heinrich Garn ◽  
...  

In the context of railway safety, it is crucial to know the positions of all trains moving along the infrastructure. In this contribution, we present an algorithm that extracts the positions of moving trains for a given point in time from Distributed Acoustic Sensing (DAS) signals. These signals are obtained by injecting light pulses into an optical fiber close to the railway tracks and measuring the Rayleigh backscatter. We show that the vibrations of moving objects can be identified and tracked in real-time yielding train positions every second. To speed up the algorithm, we describe how the calculations can partly be based on graphical processing units. The tracking quality is assessed by counting the inaccurate and lost train tracks for two different types of cable installations.


Author(s):  
Guido Ala ◽  
Giovanni Artale ◽  
Antonio Cataliotti ◽  
Valentina Cosentino ◽  
Claudio Fontana ◽  
...  

OSA Continuum ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 688
Author(s):  
Ole Henrik Waagaard ◽  
Erlend Rønnekleiv ◽  
Aksel Haukanes ◽  
Frantz Stabo-Eeg ◽  
Dag Thingbø ◽  
...  

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