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Record W2942958029 · doi:10.1144/petgeo2018-135

A feasibility study for detection thresholds of CO <sub>2</sub> at shallow depths at the CaMI Field Research Station, Newell County, Alberta, Canada

2019· article· en· W2942958029 on OpenAlexafffundabout
Marie Macquet, Don C. Lawton, Amin Saeedfar, Kirk Osadetz

Bibliographic record

VenuePetroleum Geoscience · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsCMC Research InstitutesUniversity of Calgary
FundersCenovus EnergyCanada First Research Excellence FundUniversity of Calgary
KeywordsGeologyPalaeogeographyTelmatologyMetamorphic petrologyField (mathematics)Environmental geologyRegional geologyGeobiologyEconomic geologySeismologyVolcanismTectonics

Abstract

fetched live from OpenAlex

We present the results of a feasibility study for seismic monitoring using conventional surface seismic experiments at the CaMI Field Research Station, Alberta, Canada, where a small volume of gas-phase CO 2 is being injected into a sandstone reservoir at a depth of 300 m. We first apply a careful fluid substitution procedure to the results of reservoir gas saturation and pressure responses obtained from fluid flow simulations. We test different methods to compute the bulk modulus of the fluid for different fluid saturation models. Assuming a semi-patchy model and considering only the replacement of brine with a maximum saturation of 50% CO 2 , we estimate the reduction in P-wave velocity to be 20%. Adding an increase in pore pressure of 2.7 MPa increases the P-wave velocity reduction to 32%. After including a field-based signal-to-noise ratio of 5% to the synthetic seismic data, the time-lapse seismic anomaly should be detectable after one year of injection (266 tonnes of CO 2 ).

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)0.001
metaresearch head score (Gemma)0.001
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 score0.679
Threshold uncertainty score0.639

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.273
Teacher spread0.248 · 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

Citations35
Published2019
Admission routes3
Has abstractyes

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