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Record W2936794164 · doi:10.2118/195345-ms

Application of Novel Surfactants with Co-Solvent Character for Heavy Oil Recovery

2019· article· en· W2936794164 on OpenAlexaboutno aff
Kwang Hoon Baek, Francisco J. Argüelles‐Vivas, Gayan A. Abeykoon, Ryosuke Okuno, Upali Weerasooriya

Bibliographic record

VenueSPE Western Regional Meeting · 2019
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersUniversity of Texas at Austin
KeywordsPulmonary surfactantEmulsionPolymerHydrophobeSolventChemical engineeringEthylene oxidePhenolAqueous solutionEnhanced oil recoveryChemistryMaterials scienceChromatographyOrganic chemistryCopolymer

Abstract

fetched live from OpenAlex

Abstract A new class of ultra-short hydrophobe surfactants with co-solvent character was investigated as a sole additive to conventional polymer flooding for heavy oil recovery. No alkali was used for emulsification. The surfactants tested are composed of a short hydrophobe (phenol in this research) extended by a small number of propylene oxide (PO) and sufficient ethylene oxide (EO) units to achieve aqueous stability: phenol-xPO-yEO. Results are presented for the selection of ultra-short hydrophobe surfactants, aqueous stability, emulsion phase behavior, and oil-displacement through a glass-bead pack at 368 K. Results show that 2 wt% phenol-4PO-20EO was able to reduce the interfacial tension between oil and NaCl brine to 0.39 dynes/cm, in comparison to 11 dynes/cm with no surfactant, at 368 K. Water flooding, 70-cp polymer flooding, and surfactant-improved polymer flooding were conducted for displacement of 276-cp oil through a glass-bead pack that represents the clean-sand faces of a heavy oil reservoir in Alberta, Canada. The oil recovery at 2 pore-volumes of injection was 84% with the surfactant-improved polymer flooding, which was 54% and 22 % greater than the water flooding and the polymer flooding, respectively. Results suggest a new opportunity of enhanced heavy oil recovery by adding a slug of one non-ionic surfactant with co-solvent character to conventional polymer flooding.

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 score0.036
Threshold uncertainty score0.640

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 communication0.0000.000
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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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