A field experiment of walkaway distributed acoustic sensing vertical seismic profile in a deep and deviated onshore well in Japan using a fibre optic cable deployed inside coiled tubing

2019 ◽  
Vol 68 (2) ◽  
pp. 501-520
Author(s):  
Yuki Kobayashi ◽  
Yuto Uematsu ◽  
Shimpei Mochiji ◽  
Ziqiu Xue
2020 ◽  
Author(s):  
Camilla Rasmussen ◽  
Peter H. Voss ◽  
Trine Dahl-Jensen

<p>On September 16th 2018 a Danish earthquake of local magnitude 3.7 was recorded by distributed acoustic sensing (DAS) in a ~23 km long fibre-optic cable. The data are used to study how well DAS can be used as a supplement to conventional seismological data in earthquake localisation. One of the goals in this study is extracting a small subset of traces with clear P and S phases to use in an earthquake localisation, from the 11144 traces the DAS system provide. The timing in the DAS data might not be reliable, and therefore differences in arrival times of S and P are used instead of the exact arrival times. <br>The DAS data set is generally noisy and with a low signal-to-noise ratio (SNR). It is examined whether stacking can be used to improve SNR. The SNR varies a lot along the fibre-optic cable, and at some distances, it is so small that the traces are useless. Stacking methods for improving SNR are presented.</p><p>A field test at two location sites of the fibre-optic cable was conducted with the purpose of comparing DAS data with seismometer data. At the field sites, hammer shots were recorded by a small array of three STS-2 sensors located in a line parallel to the fibre-optic cable. The recordings generally show good consistency between the two data sets. <br>In addition, the field tests are used to get a better understanding of the noise sources in the DAS recording of the earthquake. There are many sources of noise in the data set. The most prominent are a line of windmills that cross the fibre-optic cable and people walking in the building where the detector is located. Also, the coupling between the fibre-optic cable and the ground varies along the cable length due to varying soil type and wrapping around the fibre-optic cable, which is also evident in field test data. Furthermore, the data from the field tests are used to calibrate the location of the fibre-optic cable, which is necessary for using the DAS data in an earthquake localisation. <br>Data processing is done in Matlab and SEISAN.</p>


2019 ◽  
Vol 7 (1) ◽  
pp. SA11-SA19 ◽  
Author(s):  
Julia Correa ◽  
Roman Pevzner ◽  
Andrej Bona ◽  
Konstantin Tertyshnikov ◽  
Barry Freifeld ◽  
...  

Distributed acoustic sensing (DAS) can revolutionize the seismic industry by using fiber-optic cables installed permanently to acquire on-demand vertical seismic profile (VSP) data at fine spatial sampling. With this, DAS can solve some of the issues associated with conventional seismic sensors. Studies have successfully demonstrated the use of DAS on cemented fibers for monitoring applications; however, such applications on tubing-deployed fibers are relatively uncommon. Application of tubing-deployed fibers is especially useful for preexisting wells, where there is no opportunity to install a fiber behind the casing. In the CO2CRC Otway Project, we acquired a 3D DAS VSP using a standard fiber-optic cable installed on the production tubing of the injector well. We aim to analyze the quality of the 3D DAS VSP on tubing, as well as discuss lessons learned from the current DAS deployment. We find the limitations associated with the DAS on tubing, as well as ways to improve the quality of the data sets for future surveys at Otway. Due to the reduced coupling and the long fiber length (approximately 20 km), the raw DAS records indicate a high level of noise relative to the signal. Despite the limitations, the migrated 3D DAS VSP data recorded by cable installed on tubing are able to image interfaces beyond the injection depth. Furthermore, we determine that the signal-to-noise ratio might be improved by reducing the fiber length.


2016 ◽  
Vol 35 (7) ◽  
pp. 605-609 ◽  
Author(s):  
Mark E. Willis ◽  
David Barfoot ◽  
Andreas Ellmauthaler ◽  
Xiang Wu ◽  
Oscar Barrios ◽  
...  

2017 ◽  
Vol 36 (12) ◽  
pp. 987-993 ◽  
Author(s):  
Xiang Wu ◽  
Mark E. Willis ◽  
William Palacios ◽  
Andreas Ellmauthaler ◽  
Oscar Barrios ◽  
...  

2015 ◽  
Vol 3 (3) ◽  
pp. SW11-SW25 ◽  
Author(s):  
Han Wu ◽  
Wai-Fan Wong ◽  
Zhaohui Yang ◽  
Peter B. Wills ◽  
Jorge L. Lopez ◽  
...  

We have acquired and processed 3D vertical seismic profile (VSP) data recorded simultaneously in two wells using distributed acoustic sensing (DAS) during the acquisition of the 2012 Mars 4D ocean-bottom seismic survey in the deepwater Gulf of Mexico. The objectives of the project were to assess the quality of DAS data recorded in fiber-optic cables from the surface to the total depth, to demonstrate the efficacy of the DAS VSP technology in a deepwater environment, to derisk the use of the technology for future water injection or production monitoring without intervention, and to exploit the velocity information that 3D VSP data provide for evaluating and updating the velocity model. We evaluated the advantages of DAS VSP to reduce costs and intrusiveness, and we determined that high-quality images can be obtained from relatively noisy raw 3D DAS VSP data, as evidenced by the well 1 image, probably the best 3D VSP image we have ever seen. Our results also revealed that the direct arrival traveltimes can be used to assess the quality of an existing velocity model and to invert for an improved velocity model. We identified issues with the DAS acquisition and the processing steps to mitigate them and to handle problems specific to DAS VSP data. We described the steps for conditioning the data before migration, reverse time migration, and postmigration processing to reduce noise artifacts. We outlined a novel first-break picking procedure that works even in the absence of a strong first arrival and a velocity diagnosis method to assess and validate velocity models and velocity updates. Finally, we determined potential applications to 4D monitoring of fluid movement around producer or injector wells, identification of active salt movements, and more accurate imaging and monitoring of complex structures around the wells.


2021 ◽  
Author(s):  
Andreas Fichtner ◽  
Pascal Edme ◽  
Patrick Paitz ◽  
Nadja Lindner ◽  
Michael Hohl ◽  
...  

<p><span>Avalanche research requires comprehensive measurements of sudden and rapid snow mass movement that is hard to predict. Automatic cameras, radar and infrasound sensors provide valuable observations of avalanche structure and dynamic parameters, such as velocity. Recently, seismic sensors have also gained popularity, because they can monitor avalanche activity over larger spatial scales. Moreover, seismic signals elucidate rheological properties, which can be used to distinguish different types of avalanches and flow regimes. To date, however, seismic instrumentation in avalanche terrain is sparse. This limits the spatial resolution of avalanche details, needed to characterise flow regimes and maximise detection accuracy for avalanche warning.</span></p><p><span>As an alternative to conventional seismic instrumentation, we propose Distributed Acoustic Sensing (DAS) to measure avalanche-induced ground motion. DAS is based on fibre-optic technology, which has previously been used already for environmental monitoring, e.g., of snow avalanches. Thanks to recent technological advances, modern DAS interrogators allow us to measure dynamic strain along a fibre-optic cable with unprecedented temporal and spatial resolution. It therefore becomes possible to record seismic signals along many kilometres of fibre-optic cables, with a spatial resolution of a few metres, thereby creating large arrays of seismic receivers. We test this approach at an avalanche test site in the Valleé de la Sionne, in the Swiss Alps, using an existing 700 m long fibre-optic cable that is permanently installed underground for the purpose of data transfer of other, independent avalanche measurements.</span></p><p><span>During winter 2020/2021, we recorded numerous snow avalanches, including several which reached the valley bottom, travelling directly over the cable during runout. The DAS recordings show clear seismic signatures revealing individual flow surges and various phases/modes that may be associated with roll waves and avalanche arrest. We compare our observations to state-of-the-art radar and seismic measurements which ideally complement the DAS data.</span></p><p><span>Our initial analysis highlights the suitability of DAS-based monitoring and research for avalanches and other hazardous granular flows. Moreover, the clear detectability of avalanche signals using existing fibre-optic infrastructure of telecommunication networks opens the opportunity for unrivalled warning capabilities in Alpine environments.</span></p>


2020 ◽  
Author(s):  
Camille Jestin ◽  
Clément Hibert ◽  
Gaëtan Calbris ◽  
Vincent Lanticq

<p>Distributed Acoustic Sensing (DAS) is an innovative technique which has been recently employed for near-surface geophysics purposes. It involves the use of fibre-optic cable as a sensor. The fibre is analysed by sending a laser pulse from an interrogator unit. The phase of the backscattered signal contains the information on the strain on the cable, enabling the detection of a passing acoustic wave with enough energy for the cable excitation. Allowing the interrogation of long profiles and the generation of a dense spatial sampling, uneasy to obtain with classic geophysical techniques, DAS instrumentation then proved its relevance for seismic applications but also for infrastructure monitoring.</p><p>During DAS acquisition, and more precisely when closely looking at infrastructures integrity, it is necessary to clearly identify the source of the acoustic vibrations at the structure neighbourhood. Indeed, in the context of pipeline monitoring for example, it appears important to be able to classify events which generate seismic signals recorded by DAS systems and which can be related to a potential threat for the structure. In order to launch an alarm if necessary, the source identification must be fast, accurate and robust. Moreover, because DAS acquisition can generate traces every few meters along fibres of tens of kilometres, the used machine-learning algorithm must demonstrate its ability to handle a big amount of data.</p><p>In this study, we analyse the efficiency of the Random Forests (RF) machine-learning algorithm applied to data acquired with DAS system for the discrimination of event sources. RF algorithm has been selected because of its ability to handle large numbers of attributes related to signal characteristics and to enable a good reliability for the discrimination of sources. This algorithm has already proved its efficiency for automated classification of seismic waveforms (e.g. earthquakes, volcanic tremors, rock falls, avalanches, etc.).</p><p>We focus our study on tests lead along a gas pipeline instrumented with fibre-optic cable. Different third-party works have been conducted: excavation, saw sections, drill, jackhammer, etc. We work on the discrimination of six classes of seismic source. After running a detection phase based on a threshold on signal energy, we obtain several hundred of exploitable seismic traces to inject to the RF algorithm. We demonstrate the efficiency of the application of machine learning on DAS data to discriminate seismic waveforms from the correct class, with an overall precision on our test set of 99%.</p>


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