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Record W2746203294 · doi:10.1190/segam2017-17494719.1

A comparison of Alaska and Alberta heavy oil sands

2017· article· en· W2746203294 on OpenAlexaboutno aff
Hemin Yuan, De‐hua Han, Qi Huang, Qianqian Wei, Huizhong Yan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsEnvironmental sciencePetroleumGeologyMining engineeringPetroleum engineeringArchaeologyGeographyPaleontologyAsphalt

Abstract

fetched live from OpenAlex

As an important unconventional reservoir, heavy oil sands have their peculiar characteristics. Compared with conventional reservoir, heavy oil sands are usually buried at shallow depth, and the porosities are very high. Due to the temperature-sensitive viscosity of heavy oil, the velocities are highly temperature-dependent. Moreover, owing to the difference in porosity, compaction, and heavy oil viscosity, oil sands in distinct fields display difference between them. In this paper, we compared the heavy oil sands from Alaska and Alberta, and analyzed the geological factors that can affect the rock properties. Based on our samples, the depth and porosity are compared. Moreover, the variations of velocity under different temperature, and the corresponding Vp/Vs ratios are investigated. In the end, the different velocity trends of Alaska oil sands and Alberta oil sands are inspected and explicated. Presentation Date: Wednesday, September 27, 2017 Start Time: 4:45 PM Location: 351D 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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.735

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.279
Teacher spread0.252 · 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
Published2017
Admission routes1
Has abstractyes

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