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Record W3100437628 · doi:10.1002/cjce.23936

<scp>CO<sub>2</sub></scp> mobility control by small molecule thickeners during secondary and tertiary enhanced oil recovery

2020· article· en· W3100437628 on OpenAlexaffvenue
Asghar Gandomkar, Farshid Torabi, Masoud Riazi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsMiscibilityPolydimethylsiloxaneViscosityChemical engineeringEnhanced oil recoveryPolymerChemistryCarbon dioxideSurface tensionMaterials scienceCloud pointPolymer chemistryOrganic chemistryComposite materialThermodynamicsAqueous solution

Abstract

fetched live from OpenAlex

Abstract Recently, polymer thickeners have been considered for CO 2 mobility control during enhanced oil recovery (EOR) processes. Despite that, the requirement of co‐solvents is a controversial challenge for the solution of high‐molecular weight thickeners in gases. This study is focused on small molecule thickeners for carbon dioxide EOR without adding co‐solvents. Polydimethylsiloxane (PDMS) was used as a CO 2 ‐philic thickener in different low molecular weights. Cloud‐point pressure, relative viscosity, and interfacial tension (IFT) between intermediate crude oil and pure/thickened CO 2 were measured at reservoir conditions. Also, the impact of PDMS‐CO 2 thickener on gas mobility control was evaluated during the coreflooding experiments in secondary and tertiary modes. The experimental results show that PDMS caused an increase in relative viscosity up to 4.7‐fold and successfully thickened CO 2 . In addition, the minimum miscibility pressure of PDMS‐thickened CO 2 was lower than that of pure CO 2 , and miscible PDMS‐CO 2 thickener occurred at higher PDMS molecular weights. However, the gas breakthrough time can be considerably delayed if the PDMS‐thickened CO 2 was flooded directly, which increased the oil recovery factor between 6% to 15% during tertiary 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.

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 categoriesMeta-epidemiology (narrow)
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 score0.007
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.003
GPT teacher head0.160
Teacher spread0.156 · 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.

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

Citations37
Published2020
Admission routes2
Has abstractyes

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