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Record W2974710055 · doi:10.2118/196233-ms

Development and Application of Firebag SAGD Reservoir Simulation Platform

2019· article· en· W2974710055 on OpenAlexaff
Jinze Xu, Jin Wang, Hossein Aghabarati, Amir Zamani, Kathy Lai Cheung

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2019
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsSuncor Energy (Canada)
Fundersnot available
KeywordsWorkflowPetroleum engineeringReservoir simulationSteam-assisted gravity drainageReservoir modelingSteam injectionProcess (computing)Field (mathematics)Reservoir engineeringEngineeringComputer scienceGeologyOil sandsPetroleumDatabaseOperating system

Abstract

fetched live from OpenAlex

Abstract Suncor's Firebag Project is one of the largest steam-assisted gravity drainage (SAGD) projects in the world. As a powerful tool for decision-making in the field, the Firebag SAGD reservoir simulation platform is based on an in-depth understanding of physics that controls thermal recovery process and meets the need for a practical solution. In this platform, standardized inputs and workflows are developed, and a good agreement with field data is achieved for all Firebag SAGD operating pads with production history. The Firebag SAGD reservoir simulation platform promotes the capacity to address existing Firebag SAGD challenges, capture unique Firebag reservoir features, and support reservoir management and future pad development.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.146
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

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

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 head0.025
GPT teacher head0.284
Teacher spread0.259 · 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 teacher head, 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

Citations1
Published2019
Admission routes1
Has abstractyes

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