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Record W2993071932

Using a fibre-optic cable as Distributed Acoustic Sensor for Vertical Seismic Profiling - Overview of various field tests

2015· article· en· W2993071932 on OpenAlexaboutno aff
Julia Götz, Stefan Lüth, Jan Henninges, Thomas Reinsch

Bibliographic record

VenuePublication Database GFZ (GFZ German Research Centre for Geosciences) · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsProfiling (computer programming)Vertical seismic profileDistributed acoustic sensingSeismologyGeologyAcousticsOptical fiberComputer scienceTelecommunicationsFiber optic sensorPhysics
DOInot available

Abstract

fetched live from OpenAlex

Fibre-optic Distributed Acoustic Sensing (DAS) or Distributed Vibration Sensing (DVS) is a technology, where\nan optical fibre cable is used as a sensor for acoustic signals. An ambient seismic wavefield, which is coupled by\nfriction or pressure to the optical fibre, induces dynamic strain changes along the cable. The DAS/DVS technology offers the possibility to record an optoelectronic signal which is linearly related to the time dependent local strain.\nThe DAS/DVS technology is based on the established technique of phase-sensitive optical time-domain reflec-\ntometry (phi-OTDR). Coherent laser pulses are launched into the fibre to monitor changes in the resulting elastic\nRayleigh backscatter with time. Dynamic strain changes lead to small displacements of the scattering elements\n(non-uniformities within the glass structure of the optical fibre), and therefore to variations of the relative phases of the backscattered photons. The fibre behaves as a series of interferometers whose output is sensitive to small changes of the strain at any point along its length. To record the ground motion not only in space but also in time, snapshots of the wavefield are created by repeatedly firing laser pulses into the fibre at sampling frequencies much higher than seismic frequencies.\nDAS/DVS is used e.g. for continuous monitoring of pipelines, roads or borders and for production monitoring from within the wellbore. Within the last years, the DAS/DVS technology was further developed to record seismic data.\nWe focus on the recording of Vertical Seismic Profiling (VSP) data with DAS/DVS and present an overview of\nvarious field tests published between 2011 and 2014. Here, especially CO2 storage pilot sites provided the oppor-tunity to test this new technology for geophysical reservoir monitoring. DAS/DVS-VSP time-lapse measurements\nhave been published for the Quest CO2 storage site in Canada. The DAS/DVS technology was also tested at the\nCO2 storage sites in Rousse (France), Citronelle (USA), Otway (Australia) and Ketzin (Germany). At Ketzin it\nwas possible to record a multi-offset VSP simultaneously within four wells, allowing a high-resolution imaging of\nthe reservoir. These publications represent a wide range of different source types, source-receiver geometries and\ncable deployments (on tubing, behind casing).\nIn conclusion, the DAS/DVS is a promising technology for VSP acquisition; the publications showed that it is\npossible to use DAS/DVS-VSP data for the calculation of velocity profiles, for well-ties and for seismic imaging.\nSome points have to be taken into account when recording DAS/DVS-VSP data. With an optical fibre, only a single\nvalue reflecting the strain between two points within the fibre is measured. The sensitivity of the fibre-optic cable is generally lower than that of a geophone with current readout units. If a fibre-optic cable has already been installed in a well, no well intervention is needed to acquire a VSP on demand, e.g. for time-lapse measurements. Several wells can be interrogated at the same time with full vertical coverage.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.112
GPT teacher head0.373
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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