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Record W2096089020 · doi:10.1002/adv.21413

Formulation of a Self‐Assembling Polymeric Network System for Enhanced Oil Recovery Applications

2014· article· en· W2096089020 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in Polymer Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversité LavalUniversity of New Brunswick
Fundersnot available
KeywordsMaterials scienceEnhanced oil recoveryBrineChemical engineeringPulmonary surfactantAqueous solutionViscoelasticityPolymerRheologyComposite materialOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

ABSTRACT The chemical formulation and rheological properties of a novel self‐assembling polymer (SAP) network system were investigated for potential application in enhanced oil recovery (EOR). The inclusion complexes formed by surfactant (S) and β‐cyclodextrin (β‐CD) can associate with hydrolyzed polyacrylamides (HPAM) in aqueous solution, and subsequently establish the SAP network, which exhibits advanced viscoelasticity at the optimum molar ratio (S:β‐CD = 2:1). Furthermore, this system presents enhanced surface activity and superior mechanical and thermal stability, as well as tolerance to elevated brine salinity and hardness due to the network “interlocking effect”. Sandpack flood tests suggest the excellent mobility control ability of this system during polymer flooding, and also a moderate permeability reduction capacity, which makes it more cost effective in oil fields than the currently used HPAM. Regarding EOR performance, this polymeric system (SAP) produced approximately 19% more incremental oil than the baseline HPAM under the same experimental conditions.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.229
Teacher spread0.226 · 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