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Record W2037778518 · doi:10.2118/153706-pa

Test of SAGD Flow-Distribution-Control Liner System in the Surmont Field, Alberta, Canada

2013· article· en· W2037778518 on OpenAlexaffabout
John L. Stalder

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsInjectorPetroleum engineeringCasingSteam injectionEngineeringWater injection (oil production)DrillingCompletion (oil and gas wells)Oil fieldGeologyMechanical engineering

Abstract

fetched live from OpenAlex

Summary The Surmont Well Pair 102-06 started steam-assisted-gravity drainage (SAGD) operation in April 2009 with flow-distribution control (FDC) built into the horizontal liners for both the injection and the production wells. After preheat steam circulation, this liner system has allowed operation without a toe tubing string in either completion and has delivered superior steam-chamber development and overall performance. The design also allowed reducing the liner and casing size for the injection well. Future SAGD-well design may benefit by further reduction in liner and casing sizes for both the injector and producer, which could allow longer completions by reducing liner drag and increasing directional-drilling flexibility. Ultimately, these SAGD-well design changes could increase the amount of reservoir developed per well and per well pad, using longer wells and more wells per pad while achieving superior SAGD performance.

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)0.001
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 score0.115
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.003
GPT teacher head0.178
Teacher spread0.175 · 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

Citations40
Published2013
Admission routes2
Has abstractyes

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