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Record W2024522389 · doi:10.2118/146415-ms

New Solution Polymer for SAGD Production Optimization

2012· article· en· W2024522389 on OpenAlexaffabout
Jacqueline A. Behles, David Hoster, Juan Carlos Picott, Larry Sartori, Jeffry D. Pancoast, Jason R. Grinevich

Bibliographic record

VenueSPE Heavy Oil Conference Canada · 2012
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of AlbertaBaker Hughes (Canada)
Fundersnot available
KeywordsDemulsifierEmulsionPetroleum engineeringProduced waterAsphaltEnvironmental scienceSteam-assisted gravity drainageReusePipeline transportWaste managementOil productionProduction (economics)Process engineeringEngineeringMaterials scienceEnvironmental engineeringOil sandsChemical engineering

Abstract

fetched live from OpenAlex

Abstract Steam-assisted gravity drainage (SAGD) has become a method of choice for in-situ production of bitumen for oil sands projects in Alberta, Canada. Fluid production using SAGD results in the formation of a high water cut complex emulsion requiring the use of both reverse emulsion breakers and emulsion breakers in order to produce saleable oil to the pipeline and clean water for reuse or steam generation. As a facility grows and adds more well pairs to the system, maintaining the water quality from the separation vessels becomes increasingly important to growing the daily bitumen production. The enclosed paper will detail the development of a new solution polymer designed to treat the produced complex emulsion. Through an improved synergy with the demulsifier, the newly developed reverse emulsion breaker reduced the required levels of demulsifier to treat the fluids while maintaining or improving the system water qualities observed.

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: Empirical
Teacher disagreement score1.000
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.234
Teacher spread0.214 · 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

Citations1
Published2012
Admission routes2
Has abstractyes

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