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Record W3105247110 · doi:10.1021/acsomega.0c04464

Mechanistic Investigation of LSW/Surfactant/Alkali Synergism for Enhanced Oil Recovery: Fluid–Fluid Interactions

2020· article· en· W3105247110 on OpenAlexaff
Ali Esfandiarian, Amin Azdarpour, Rafael M. Santos, Erfan Mohammadian, Hossein Hamidi, Parham Babakhani Dehkordi

Bibliographic record

VenueACS Omega · 2020
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPulmonary surfactantChemistryAlkali metalSodium dodecylbenzenesulfonateCationic polymerizationBrineAqueous solutionMicromodelSurface tensionEnhanced oil recoveryChemical engineeringDrop (telecommunication)Critical micelle concentrationBromideFourier transform infrared spectroscopyMicelleInorganic chemistryChromatographyOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The combination of chemical enhanced oil recovery (CEOR) and low salinity water (LSW) flooding is one of the most attractive enhanced oil recovery (EOR) methods. While several studies on CEOR have been performed to date, there still exists a lack of mechanistic understanding on the synergism between surfactant, alkali and LSW. This synergism, in terms of fluid–fluid interactions, is experimentally investigated in this study, and mechanistic understanding is gained through fluid analysis techniques. Two surfactants, one cationic and one anionic, namely an alkyltrimethylammonium bromide (C 19 TAB) and sodium dodecylbenzenesulfonate (SDBS), were tested, together with NaOH used as the alkali, diluted formation brine used as the LSW, and the crude oil was collected from an Iranian carbonate oil reservoir. Fluids were analyzed using pendant drop method for interfacial tension (IFT) measurement, and Fourier transform infrared spectroscopy for determination of aqueous and oleic phase chemical interaction. The optimum concentration of LSW for IFT reduction was investigated to be 1000 ppm. Additionally, both surfactants reduced IFT significantly, from 28.86 mN/m to well below 0.80 mN/m, but in the presence of optimal alkali concentration the IFT dropped further to below 0.30 mN/m. IFT reduction by alkali was linked to the production of three different types of in situ anionic surfactants, while in the case of anionic and cationic surfactants, saponification reactions and the formation of the C 19 TAOH alcohol, respectively, were linked to IFT reduction. The critical micelle concentration and optimal alkali concentration when using cationic C 19 TAB were significantly lower than with the anionic surfactant; respectively: 335 vs 5000 ppm, and 500 vs 5000 ppm. However, it was found that SDBS was more compatible with NaOH than C 19 TAB, due to occurrence of alkali deposition with the latter beyond the optimal point.

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 score0.009

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)0.0030.000

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 head0.018
GPT teacher head0.236
Teacher spread0.217 · 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".

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Citations33
Published2020
Admission routes1
Has abstractyes

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