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Record W2789815275 · doi:10.2118/0318-0020-jpt

Technology Update: Improving Corrosion Management by Using a Micelle Detection Technology

2018· article· en· W2789815275 on OpenAlexaff
Scott Rankin, Andrew Osnowski, Fiona Mackay, Emma Perfect, Mohsen Achour, David Blumer

Bibliographic record

VenueJournal of Petroleum Technology · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsCorrosionCorrosion inhibitorCorrosion monitoringMicelleComputer scienceDosingChemistryEnvironmental scienceProcess engineeringEngineeringAqueous solutionOrganic chemistry

Abstract

fetched live from OpenAlex

Technology Update Corrosion inhibitors are often the first line of defense against internal corrosion, and effective mitigation relies on proactive monitoring and management of these inhibitors to allow for regular feedback and dose adjustment. There have been recent developments in this field, for example in greener chemistries (NACE 2016-7738) and treatment methods (Achour et al. 2008) and a growth in the number of papers published in OnePetro. We found an 18% increase in the number of publications in the last 10 years, compared with the previous 10, when searching the terms “corrosion AND inhibitor”. Nevertheless, there is room for improvement. Common industry opinion is that current residual monitoring methods, such as colorimetric-complex methods or liquid chromatography mass spectrometry are either not sufficiently reliable for effective inhibitor dosage management or too complex to apply in the field. Yet there is a significant commercial driver, as dosing corrosion inhibitors is an expensive undertaking and the potential cost improvements from improved management are significant. One North Sea operator reported that it believed it was overdosing inhibitor at an extra cost of pound 400,000 per year. An alternative corrosion inhibitor monitoring approach, first published in 2011 (NACE 11071), exploits the formation of corrosion inhibitor micelles and poses the questions: Does micelle detection have a place in the inhibitor qualification process? Can it help inform chemical management in the field? Micelles—a Recap The most common class of corrosion inhibitor used in the oilfield is amphiphilic surfactant molecules, which form a barrier on the pipe surface to protect from corrosion. Above a certain concentration, nanoscale aggregates called corrosion inhibitor micelles are formed. This point is called the Critical Micelle Concentration (CMC) and is specific to the chemical and unique physical conditions found in each system. The CMC has been shown to be an important factor in establishing an effective inhibitor dose, with the optimum dose being equivalent to the CMC (e.g. NACE 10326). Below the CMC, there is an opportunity to dose additional inhibitor to further reduce corrosion while above the CMC, surplus inhibitor may be present. Earlier studies looking at CMC and its relation to optimal inhibitor dose were relatively simplistic laboratory studies. Out of the 43 CMC studies reviewed, only one study used formulated inhibitors, with the majority looking only at single components, e.g. an imidazoline. Importantly, all 43 studies used model systems to test the corrosion inhibitors, rather than complex field fluids. It is therefore pertinent to address the question of whether micelles and the significance of the CMC are still relevant in real field 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0020.001
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0260.022

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 head0.242
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations3
Published2018
Admission routes1
Has abstractyes

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