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Record W4212931986 · doi:10.4133/1.2963250

VSP and Well Logs from a Shallow Test Well

2008· article· en· W4212931986 on OpenAlexaffabout
Joe Wong, Soo K. Miong, L. R. Bentley, Robert R. Stewart

Bibliographic record

VenueSymposium on the Application of Geophysics to Engineering and Environmental Problems 2008 · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGeologyGeophoneDrillingWell loggingOil shaleHydrogeologySeismologyGeophysicsGeotechnical engineeringPaleontologyEngineering

Abstract

fetched live from OpenAlex

Shallow VSP and geophysical well logging surveys were undertaken in a 127 m-deep well drilled at the Rothney Astrophysical Observatory site near Priddis, Alberta. The well was drilled through interbedded sands and shales of the Tertiary Paskapoo formation. A suite of geophysical well logs (including natural gamma-ray, single-point resistance, focused-beam resistivity, density, neutron-neutron, caliper, temperature, and SP) was acquired in the open hole immediately after drilling. After PVC casing was inserted into the well and grouted to the formation rocks, we obtained natural gammaray and full-waveform sonic logs. The logs were useful for delineating the sandstone and shale beds, and for providing hydrogeological information at the well site. For the VSP surveys, we used a 5.5 kg sledge hammer source with a downhole clamping 3C geophone. From the full-waveform sonic logs and VSP data, we determined P-wave velocities to lie between 1.7 to 3.0 km/s. Standard VSP processing resulted in a corridor stack of up-going reflections that correlate reasonably well with a 1-D synthetic seismogram constructed from the density and P-wave velocity logs.

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)0.001
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 score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.149
Teacher spread0.144 · 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

Citations1
Published2008
Admission routes2
Has abstractyes

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