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Record W2073339654 · doi:10.1080/10916460701208348

Experimental Investigation of Surfactant Partition in Heavy Oil/Water/Sand Systems

2008· article· en· W2073339654 on OpenAlex
Wen Zhou, Mingzhe Dong, Q. Liu, Han Xiao

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePetroleum Science and Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of New BrunswickUniversity of Regina
FundersPetroleum Technology Research CentreNorth Dakota State University
KeywordsPulmonary surfactantAdsorptionChemistryChemical engineeringChromatographyEnhanced oil recoveryPetroleum engineeringGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Surfactant adsorption on reservoir rocks or sands is one of the major factors that may significantly reduce the effectiveness of an alkaline/surfactant flood for oil recovery. It is difficult to determine the surfactant adsorption by measuring the difference between surfactant concentrations before and after adsorption when the water phase contains fine oil drops. In this study, an extraction method was used to quantitatively determine the adsorptions of surfactant on sand and at oil-water interfaces in an alkaline/surfactant flood for heavy oil recovery. Experimental results showed that the formation of emulsions dramatically reduced surfactant loss to sand surface. The adsorptions of surfactant on sand and at oil-water interface were determined under various alkaline concentrations and salinities. The results provide useful information for evaluating and predicting surfactant adsorption in alkaline/surfactant flooding for enhanced heavy oil recovery.

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.

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 score0.008
Threshold uncertainty score0.276

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 studies0.0000.001
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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