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Record W2061968538 · doi:10.1080/10916460701287557

An Environment-Friendly Alkaline Solution for Enhanced Oil Recovery

2008· article· en· W2061968538 on OpenAlexaff
M. Safiur Rahman, M. Enamul Hossain, M. R. Islam

Bibliographic record

VenuePetroleum Science and Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAlkali metalEnvironmentally friendlyCoalescence (physics)ChemistrySurface tensionChemical engineeringEnhanced oil recoveryAcid valueOil productionPolymerOrganic chemistryPetroleum engineeringThermodynamicsGeology

Abstract

fetched live from OpenAlex

The injection of alkali and alkali/polymer solutions is a well-known enhanced oil recovery technique. This article demonstrates how wood ash can be used as a source of low cost alkali instead of synthetic alkali that is also environmentally friendly. From the experimental studies, it is found that the pH value of 6% wood ash extracted solution is very close to the pH value of 0.5% synthetic NaOH or of 0.75% Na2SiO3 solution. A preliminary microscopic study of oil/oil droplets interaction in natural alkaline solution was carried out in order to understand the oil/water interface changes with time and its effect on oil/oil droplet coalescence. Also, interfacial tension (IFT) was measured for both synthetic and natural alkaline solutions. The IFT values in the presence of acidic crude oil show comparable results.

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 score0.001
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 head0.007
GPT teacher head0.216
Teacher spread0.210 · 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

Citations10
Published2008
Admission routes1
Has abstractyes

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