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Record W2897207135 · doi:10.3997/2214-4609.201801472

Initial Results of Time-Lapse Processing of VSP Geophone and DAS Fiber-Optic Cable at Aquistore CO2 Injection Site, Sask

2018· article· en· W2897207135 on OpenAlexaffabout
Saeid Cheraghi, Daniel White, Kyle Harris, Brian J. Roberts

Bibliographic record

VenueProceedings · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsGeophoneGeologyVertical seismic profilePlumeSeismologyPetroleum engineeringMeteorology

Abstract

fetched live from OpenAlex

Summary The Aquistore CO2 storage site is located near the town of Estevan, Saskatchewan, Canada. The CO2 reservoir is located at a depth of about 3300 m, and injection and observation wells are drilled down to that depth. A coal-fired power plant east of the wells supplies the CO2 and a pipeline transfers the CO2 to the injection well. Prior to injection, vertical seismic profiles (VSPs) were acquired in November 2013 with two receiver types: a 60-level geophone tool deployed at a depth range of 1650–2650 m, and a fiber-optic cable used for distributed acoustic sensing (DAS) installed from the surface to a depth of 2766 m. CO2 injection started in April 2015 with a planned injection rate of 500–600 ton/day. In February 2016 about 36 kilotonnes of CO2 was injected when the first monitor VSP geophone and DAS data were acquired. The second DAS monitor survey was acquired in November 2016 when about 102 kilotonnes of CO2 was injected. We have applied a similar processing flow to all vintages of both geophone and DAS data to evaluate how repeatable VSPs can image the CO2 plume.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.225
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2018
Admission routes2
Has abstractyes

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