Test Methodologies and Field Verification of Corrosion Inhibitors to Address under Deposit Corrosion in Oil and Gas Production Systems
Bibliographic record
Abstract
Abstract A research program, suggested by an oil producer was initiated to develop and test corrosion inhibitors having good performance in pipelines suffering from corrosion associated with deposited solids. The program comprised the development of candidate DSTI’s (Deposited Solids Tolerant Inhibitors) using column adsorption tests, which were then subjected to an inhibition performance test, also had to be developed. The oil producer defined its needs and offered pipelines for field verification. A specific inhibitor (designated C) has been identified as a good DSTI, having superior performance over the incumbent inhibitor in the oil producer's sweet pipelines system. The laboratory inhibition performance test developed enables the simultaneous testing of inhibitors for their performance in regular (non solids covered) systems and underneath solids. A field verification program has been scheduled to obtain the final confirmation before full deployment of Inhibitor C in the oil producer’s solids bearing lines. To that end the Field Corrosivity Toolbox has been modified to enable the performance testing of DSTI’s underneath solids.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".