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Record W2020903517 · doi:10.2118/117489-ms

SAGD Gas Lift Completions and Optimization: A Field Case Study at Surmont

2008· article· en· W2020903517 on OpenAlexaff
T. C. Handfield, T. Nations, Shauna Noonan

Bibliographic record

VenueInternational Thermal Operations and Heavy Oil Symposium · 2008
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsConocoPhillips (Canada)
FundersConocoPhillips
KeywordsGas liftLift (data mining)Petroleum engineeringArtificial liftNozzleSluggingEngineeringNatural gas fieldCompletion (oil and gas wells)Natural gasMechanical engineeringEnvironmental scienceMarine engineeringComputer scienceWaste managementMechanicsFlow (mathematics)

Abstract

fetched live from OpenAlex

Abstract Gas lift completions for SAGD1 producers are unique. Conventional gas lift valves and mandrels with a packer completion cannot be used due to the extreme temperatures of the downhole environment. Most lift gas enters the production stream downhole via open-ended tubing or nozzles, which if not properly sized can result in operational issues, such as fluid / gas slugging and pressure instabilities which negatively impact the overall lift efficiency. In 2006, ConocoPhillips conducted a study to design a gas lift system for the Surmont SAGD development that would allow better control of lift gas into the production string and in late 2007 the wells completed with gas lift were placed on production. This paper will cover the data collection effort and analysis completed to determine the efficiency of the two types of gas lift nozzles used in the completions, the methodology for optimization of SAGD gas lift systems and recommendations for future improvement.

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)0.002
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 score0.114
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

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