Corrosion Inhibitors for Sweet Oilfield Environment (<scp>CO</scp><sub>2</sub>Corrosion)
Bibliographic record
Abstract
Corrosion of metallic structures in the presence of specific gases has been recognized as a significant problem in the oilfield industries, both in production and transportation. While sour corrosion is a more drastic form of corrosion in the presence of sulfide gas and sulfuric acid, sweet corrosion is its mild counterpart that occurs in the presence of carbon dioxide (CO2) gas. However, the word “mild” is used in a relative term since sweet corrosion could be very aggressive depending on the amount of CO2 dissolved within the medium. The use of chemical corrosion inhibitors is one the most effective CO2 corrosion mitigation techniques for carbon steel in oil and gas production. In recent times, some small organic molecules (e.g. amines, imidazolines, etc.) as well as polymers (e.g. natural and synthetic polymers) have been used to inhibit CO2 corrosion within internal pipelines. In this work, we have identified safer inhibitor products as well as factors that promote their adsorption on metal surfaces toward enhanced corrosion inhibition. The possession of multiple adsorption sites on these molecules have been vividly explained in line with short- and long-term corrosion remediation. This work has also extensively addressed accompanied reaction mechanisms in line with the types of causative agents in the petroleum industries. Practical scenarios are also drawn from experimental projects reporting the effects of sweet corrosion on metallic structures deployed in oilfields.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".