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Record W4401703434 · doi:10.29169/1927-5129.2024.20.10

Application and Optimization of Rheological Mathematical Models in Preparation and Injection Process of Polymer Flooding

2024· article· en· W4401703434 on OpenAlexvenueno aff
Shijie Zhu, Jiacheng Tang, Mei Xu, Zhuang Zhuang Huang, Shuang Lai Yang, Yang Wang, Yong Zhu

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2024
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRheologyProcess (computing)PolymerFlooding (psychology)Petroleum engineeringMaterials scienceProcess engineeringComputer scienceEngineeringComposite materialPsychology

Abstract

fetched live from OpenAlex

The impact of shear on the properties of polymer solutions is not fully accounted for in the simulation of conventional rheological models. To optimize the application of these mathematical models, the shear rheological characteristics of polyacrylamide at various concentrations were investigated under different shear rates and shear modes. The results indicate that pseudoplastic fluid polymers exhibit two distinct rheological characteristics within their "shear thinning" rate range. The critical shear rate at this threshold signifies the point at which shear stress begins to disrupt the structure of the polymer solution, resulting in a rheological curve that transitions from high to low shear rates without reverting back to a low-to-high state. The concentration of the solution has minimal effect on the critical shear rate of the polymer, which is primarily determined by the inherent properties of the polymer itself. The application of rheological models during preparation and injection processes can elucidate the effects of shear on polymers by analyzing changes in apparent viscosity. Furthermore, the rheological model following supercritical shear rates requires modifications to the consistency coefficient (K) and flow index (n) based on shear rheological data obtained from high to low shear rates.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.171

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.269
Teacher spread0.257 · 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 designSimulation or modeling
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

Citations1
Published2024
Admission routes1
Has abstractyes

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