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Record W3139417659 · doi:10.1021/acsomega.1c00026

Conformance Control in Oil Reservoirs by Citric Acid-Coated Magnetite Nanoparticles

2021· article· en· W3139417659 on OpenAlexaff
Hassan Divandari, Abdolhossein Hemmati‐Sarapardeh, Mahin Schaffie, Maen M. Husein, Mohammad Ranjbar

Bibliographic record

VenueACS Omega · 2021
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChemical engineeringPolyacrylamideMaterials scienceResidual oilDynamic light scatteringCitric acidMicromodelNanoparticleFourier transform infrared spectroscopyMagnetorheological fluidEnvironmentally friendlyMagnetiteOil fieldEnhanced oil recoveryChemistryNanotechnologyPetroleum engineeringComposite materialMagnetic fieldOrganic chemistryPolymer chemistryMetallurgyGeology

Abstract

fetched live from OpenAlex

NPs of an average diameter of 40 nm, well coated with citric acid. CACM NPs provide a high pressure drop coupled with an acceptable resistance factor and residual resistance factor owing to NP arrangement into a solid-/gel-like structure in the presence of a magnetic field. A resistance factor and a residual resistance factor of 3.5 and 2.14, respectively, were achieved for heavy oil and the heterogeneous micromodel. This structure contributed to an appreciable plugging efficiency. CACM NPs respond to ∼1000 G of magnetic field intensity and display a constant resistance factor at intensities between 4500 and 6000 G. CACM NPs act as a gel, forming a solid-/gel-like structure, which moves toward the magnetic field and thereby shuts off the produced water and increases the oil fraction. The findings of this study suggest the ability to shut off water production using specially designed magnetic field-responsive smart fluids. The application would require innovative design of field equipment.

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 score0.019
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.202
Teacher spread0.197 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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