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Record W2050392097 · doi:10.2118/100599-ms

Use of Silicate-Based Drilling Fluids To Mitigate Metal Corrosion

2007· article· en· W2050392097 on OpenAlexaboutno aff
Michael McDonald, K. P. Barr, S. R. Dubberley, G. R. Wadsworth

Bibliographic record

VenueInternational Symposium on Oilfield Chemistry · 2007
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
Fundersnot available
KeywordsCorrosionSodium silicateSilicateDrilling fluidPotassiumMetalGravimetric analysisMetallurgyPotassium silicateMaterials scienceDrillingSodiumChemistryChemical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Since the re-introduction of silicate-based drilling fluids in the mid-1990's, there have been numerous papers written on the inhibitive and environmental properties of sodium and potassium silicate. While it is generally accepted that potassium or sodium based silicate drilling fluids will help mitigate metal corrosion, there appears to be very little written on the subject. With increasing steel prices and competition for drill pipe, operators are becoming more aware of the cost associated with metal corrosion. This paper discusses the chemistry of sodium and potassium silicate as it relates to the protection of metals against corrosion. Aside from providing an alkaline environment, silicate is shown to deposit a protective film on various metal surfaces. Drill string corrosion coupons were used to gauge the amount of wear and corrosion while running potassium silicate-based drilling fluids in Western Canada. Gravimetric analysis confirmed that the rate of metal corrosion was minimal under a variety of drilling conditions.

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 score0.022
Threshold uncertainty score0.619

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 head0.016
GPT teacher head0.237
Teacher spread0.221 · 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

Citations9
Published2007
Admission routes1
Has abstractyes

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