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Record W4412833210 · doi:10.3390/polym17152033

Impact of Adverse Mobility Ratio on Oil Mobilization by Polymer Flooding

2025· article· en· W4412833210 on OpenAlexfundno aff
A.B.M. Wahid Murad, Arne Skauge, Behruz Shaker Shiran, Tormod Skauge, Alexandra Klimenko, Enric Santanach‐Carreras, Stéphane Jouenne

Bibliographic record

VenuePolymers · 2025
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersTotalEnergi SimulationHeriot-Watt University
KeywordsPolymerProduced waterEnhanced oil recoveryMaterials scienceRheologyExtraction (chemistry)Environmental scienceChemical engineeringPetroleum engineeringPulp and paper industryEnvironmental engineeringChemistryChromatographyComposite materialGeology

Abstract

fetched live from OpenAlex

Polymer flooding is a widely used enhanced oil recovery (EOR) method for improving energy efficiency and reducing the carbon footprint of oil production. Optimizing polymer concentration is critical for maximizing recovery while minimizing economic and environmental costs. Here, we present a systematic experimental study which shows that even very low concentrations of polymers yield relatively high recovery rates at adverse mobility ratios (230 cP oil). A series of core flood experiments were conducted on Bentheimer sandstone rock, with polymer concentrations ranging from 40 ppm (1.35 cP) to 600 ppm (10.0 cP). Beyond a mobility ratio threshold, increasing polymer concentration did not significantly enhance recovery. This plateau in performance was attributed to the persistence of viscous fingering and oil crossflow into pre-established water channels. The study suggests that low concentrations of polymer may mobilize oil at high mobility ratios by making use of the pre-established water channels as transport paths for the oil and that the rheology of the polymer enhances this effect. These findings enable reductions in the polymer concentration in fields with adverse mobility ratios, leading to substantial reductions in chemical usage, energy consumption, and environmental impact of the extraction process.

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 score0.087
Threshold uncertainty score0.642

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 head0.005
GPT teacher head0.257
Teacher spread0.252 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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