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Record W2053903586 · doi:10.1080/15567031003753553

A Comprehensive Evaluation of SAGD and Fast-SAGD Application in Three Major Formations of Alberta's Oil Sand

2013· article· en· W2053903586 on OpenAlexaboutno aff
T. B. N. Nguyen, Tiffany Dang, Wisup Bae

Bibliographic record

VenueEnergy Sources Part A Recovery Utilization and Environmental Effects · 2013
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringAsphaltOil sandsSteam-assisted gravity drainageGeologyOffset (computer science)Computer scienceMaterials science

Abstract

fetched live from OpenAlex

This article presented a comparative evaluation between the conventional SAGD and the Fast-SAGD application in three typical formations of Alberta's Oil Sand that included McMurray, Clearwater, and Bluesky formations. Three reservoir models with a series of numerical simulations under various operation conditions were developed to achieve the most unprejudiced comparison between two bitumen recovery processes. The simulation results proved that significantly recoverable bitumen was produced from offset wells in the Fast-SAGD system and leads to a higher recovery factor. There is only a slight increase in cumulative oil recovery when two processes were performed in the same pattern with a similar number of production wells. The results also indicated that the difference of 10 kPa between steam injection pressure and reservoir pressure in the literature is not enough for both SAGD and Fast-SAGD operations. Finally, a precious guideline was recommended in order to achieve the most success on SAGD and Fast-SAGD application at those formations.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.150

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 studies0.0010.000
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 head0.012
GPT teacher head0.224
Teacher spread0.211 · 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 designSimulation or modeling
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

Citations6
Published2013
Admission routes1
Has abstractyes

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