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Record W2973628257

Improved oil recovery by reducing surfactant adsorption with polyelectrolyte in high saline brine A Physicochemical and engineering aspects

2016· article· en· W2973628257 on OpenAlexaboutno aff
Mahesh Budhathoki, Sai Hari Ram Barnee, Bor‐Jier Shiau, Jeffrey H. Harwell

Bibliographic record

VenueColloids and Surfaces · 2016
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPulmonary surfactantBrineAdsorptionPolystyrene sulfonatePolyelectrolyteEnhanced oil recoveryChemical engineeringChemistrySulfonateChromatographySodiumOrganic chemistryPolymer
DOInot available

Abstract

fetched live from OpenAlex

Surfactant adsorption on reservoir rock is one of the biggest challenges of chemical enhanced oil recovery (cEOR) techniques. This problem can become severe in high saline brine environments. In this work, the efficacy of a polyelectrolyte, polystyrene sulfonate (PSS), as a sacrificial agent for lowering surfactant adsorption from reservoir brine that has totally dissolved solids (TDS) of over 300,000mg/l is investigated. Four different molecular weight PSSs are evaluated through equilibrium and dynamic adsorption studies carried out on Berea sandstone and Ottawa sand. Results show significant reduction in surfactant adsorption after PSSs addition. The effects of surfactant/PSS addition techniques, sequential and simultaneous, on surfactant and/or PSS adsorptions are also studied. Sand pack studies are conducted to evaluate the effect of PSS-minimized surfactant adsorption on oil mobilization/recovery. Results show substantial improvement in oil recovery in the presence of PSS, suggesting a potential as a sacrificial agent in cEOR.

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 score0.003
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.002
GPT teacher head0.168
Teacher spread0.166 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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