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Record W306267682 · doi:10.5006/c2004-04395

Scale Inhibitor Developments Providing Mitigating Benefits against Mineral Scale Deposition in Platform Oil Processing Equipment Operating on Canada’S Atlantic Coast

2004· article· en· W306267682 on OpenAlexaboutno aff
M.R. Gregg, S. Oates, John L. Przybylinski, H. H. Downs

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsnot available
Fundersnot available
KeywordsScale (ratio)Environmental scienceDeposition (geology)Mineral oilPetroleum engineeringMarine engineeringEngineeringGeologyMetallurgyMaterials scienceSedimentGeography

Abstract

fetched live from OpenAlex

Abstract New scale inhibitor chemistries were evaluated for providing mitigating benefits against calcite and iron sulfide scale deposition using a high pressure capillary tube blocking apparatus (CTBA). The tests were conducted using brine, with and without iron sulfide contaminants dissolved into the fluid. The CTBA study provided direction on which chemistries work best when iron sulfide poisoning is prevalent. Best performing products selected in the CTBA protocol were re-evaluated for mitigating benefits on calcite precipitation induction time tests. In the induction study fine particles of pyrite deposits (FeS2) and/or finely dispersed sand (SiO2) were dispersed into the brine. This latter technique more closely models and evaluates the field brine scaling conditions, with and without scale inhibitor addition. The combined study was effective in detailing the root cause analysis for the offshore oil producing platforms equipment fouling mechanism. The study was also useful for identifying candidates for field trial. Subsequent field monitoring data indicates the new products provide mitigating benefits against calcite and pyrite mineral scale deposition, particularly within the crude oil run down coolers.

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 score0.502
Threshold uncertainty score0.947

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 communication0.0000.001
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 head0.015
GPT teacher head0.224
Teacher spread0.209 · 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
Published2004
Admission routes1
Has abstractyes

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