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Record W2911602322 · doi:10.4236/aces.2019.91007

Investigation of Different Ionic Liquids in Improving Oil Recovery Factor

2019· article· en· W2911602322 on OpenAlexaff
Ali Alarbah, Mohamed Shirif, Ezeddin Shirif

Bibliographic record

VenueAdvances in Chemical Engineering and Science · 2019
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsPetroleum Technology Research CentreUniversity of Regina
Fundersnot available
KeywordsIonic liquidSurface tensionEnhanced oil recoveryChlorideViscosityChemistryWettingChemical engineeringChromatographyPetroleum engineeringMaterials scienceOrganic chemistryGeologyThermodynamicsComposite materialEngineering

Abstract

fetched live from OpenAlex

In order to improve oil recovery, Enhanced Oil Recovery (EOR) techniques have been applied to several light and medium oil reservoirs. This research was directed towards the development of chemical flooding methods for such reservoirs. The main objective of this experimental work was to investigate the efficiency of introducing various types of Ionic Liquids (ILs), 1-Ethyl-3-methylimidazolium Chloride [EMIM][Cl], 1-Benzyl-3-methylimidazolium Chloride [BenzMIM][Cl], and Trihexyltetradecylphosphonium Chloride [THTDPh][Cl] on the Recovery Factor (RF) of medium oil (Weyburn oil, 30.25 API°) at room temperature. The series of flooding experiments were carried out by introducing a slug of IL mixtures. Results demonstrated that maximum oil recovery factor was obtained when [EMIM][Cl] was added in the displacing fluid. Further investigations have been conducted to examine the effect of ILs concentrations on the recovery mechanisms by measuring Surface Tension (SFT), pH, and viscosity of the displacing phases. Finally, the effect of theses ILs in wettability alteration was examined.

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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.004
GPT teacher head0.204
Teacher spread0.200 · 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 teacher head, 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

Citations6
Published2019
Admission routes1
Has abstractyes

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