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Record W4416716508 · doi:10.1002/cjce.70179

Influence of different minerals of sandstone rocks on the retention of scale inhibitor

2025· article· en· W4416716508 on OpenAlexvenueno aff
Carolina B. Veloso, Rafael T. Landim, F. Murilo T. Luna, Célio L. Cavalcante

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsnot available
FundersFundação Cearense de Apoio ao Desenvolvimento Científico e TecnológicoPetrobrasConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsFeldsparBiotiteScale (ratio)MineralMatrix (chemical analysis)Phase (matter)

Abstract

fetched live from OpenAlex

Abstract An effective method for controlling scale formation near production wells is the squeeze treatment using a scale inhibitor. The scale inhibitor injected into the well is retained in the rock matrix and gradually released during the oil production, preventing scale formation. This study explored the interaction between a scale inhibitor, aminotrimethylene phosphonic acid (ATMP), and some mineralogical components of sandstone formations using batch experiments. The experiments were performed for each individual pure mineral phase (quartz, feldspar, kaolin, and biotite), as well as for combinations of these phases, in order to determine whether the retention curves for mixed systems may be derived from the curves of the individual constituents. Moreover, the impact of temperature and pH on the inhibitor retention mechanisms (adsorption and/or precipitation) was also evaluated. Biotite exhibited the highest retention among the evaluated pure minerals (70 mg/g), while aluminium released from feldspar and kaolin influenced retention through complexation with the inhibitor. These results emphasize the critical role of mineralogical composition in scale inhibitor retention and support the selection and formulation of more effective squeeze treatments in sandstone reservoirs.

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 score0.026
Threshold uncertainty score0.162

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 head0.007
GPT teacher head0.197
Teacher spread0.189 · 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

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicCalcium Carbonate Crystallization and InhibitionFrench-language works237,207