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Record W2147923900 · doi:10.1002/cjce.5450820424

A Novel Approach for Resolving Reverse Emulsions in SAGD Production Systems

2004· article· en· W2147923900 on OpenAlexaffvenue
Ron Bosch, Eric Axcell, Virgil Little, Robert W. Cleary, Sanyi Wang, Robert A. Gabel, Bill Moreland

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsCumulative Environmental Management Association
Fundersnot available
KeywordsEmulsionProduced waterDownstream (manufacturing)Environmental scienceOil productionPetroleum engineeringWaste managementProcess engineeringEnvironmental engineeringGeologyEngineeringChemical engineeringOperations management

Abstract

fetched live from OpenAlex

Abstract The standard method for water clarification in SAGD operations involved the injection of latex polymers to break produced reverse emulsions. Operationally, this resulted in large quantities of oil in the water downstream of the first oil and water separation vessel. Problems occurred because this generated large amounts of oily solids and emulsion. This slop material represented a significant additional expenditure. Champion developed a novel approach to treat these systems and reduce slop. This program has been very successful in removing oil from the water stream during primary separation, thus reducing costs associated with reprocessing slop oil, and resulting in greater oil recovery.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.182
Teacher spread0.170 · 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 designBench or experimental
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

Citations22
Published2004
Admission routes2
Has abstractyes

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