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Record W2508788701 · doi:10.1190/segam2016-13842507.1

From exploration to reclamation: Using EM methods at SAGD sites in the Athabasca oil sands

2016· article· en· W2508788701 on OpenAlexaff
Sarah G. R. Devriese, Douglas W. Oldenburg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOil sandsLand reclamationPetroleum engineeringUnconventional oilGeologyMining engineeringSteam-assisted gravity drainageOil explorationEnvironmental scienceAsphaltArchaeologyGeographyOil shale

Abstract

fetched live from OpenAlex

In this paper, we divide the steps to explore, identify, and monitor a SAGD site into six stages and show how electromagnetic methods can be used at each stage. Three-dimensional inversion of airborne EM data provide large-scale, regional geologic trends and delineate paleo-channels and the caprock thickness at a newly-developed property in the Athabasca oil sands. We use semi-synthetic models from resistivity logging and the airborne data in conjunction with ground-based and borehole EM surveys to characterize the oil-rich McMurray Formation and monitor steam chamber growth over time. Periodic EM data collection and three-dimensional time-lapse inversion allow for high-resolution interpretations throughout the SAGD process. Presentation Date: Wednesday, October 19, 2016 Start Time: 4:00:00 PM Location: 174 Presentation Type: ORAL

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.079
GPT teacher head0.326
Teacher spread0.246 · 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
Published2016
Admission routes1
Has abstractyes

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