The Development of Novel Corrosion Inhibitors for High Temperature Sour Gas Environments
Bibliographic record
Abstract
Abstract Mitigating oil and gas production with chemical inhibitors is challenging when high temperature (>120°C) and H2S is present. The high temperatures associated with deep wells and thermal recovery methods demand an advancement in conventional inhibitor technologies. Traditional organic inhibitors struggle to protect carbon steel assets lending them susceptible to localized corrosion in sour environments. These environments require inhibitors with a combined thermal stability and persistency to provide uniform filming and corrosion protection. For high temperature corrosion applications imidazoline chemistry ranks highly as a chemistry likely to be able to mitigate corrosion at elevated temperatures. However, at temperatures between 120 and 150°C performance is very system specific while over 150°C performance can be severely limited. An extensive in-house screening program was undertaken which identified a generic chemistry (pyrimidine) that exhibited the required performance characteristics up to 175°C for a variety of field applications. Based on this work, several other materials exhibited performance benefits for alternate applications, for instance high temperature, deep water applications. Laboratory testing of the novel corrosion inhibitors at high temperatures, also highlighted the limitations of corrosion test methodologies for evaluating inhibitors under extreme conditions.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".