Application of Electrochemical Noise Monitoring to Inhibitor Evaluation and Optimization in the Field: Results from the Kaybob South Sour Gas Field
Bibliographic record
Abstract
Abstract Electrochemical noise monitoring produces real time corrosion data, which provides information both on the level of corrosion activity in a system and the dominant corrosion mechanism. This data can be used to efficiently evaluate corrosion inhibitor effectiveness and to optimize injection rates. This paper will present data obtained in Canada's Kaybob South Sour Gas field during inhibitor evaluation and optimization testing. Details of the field equipment setup and the data analysis process will be presented along with conclusions regarding inhibitor effectiveness and the field use of electrochemical noise monitoring for inhibitor evaluation. The inhibitor testing completed in the Kaybob South field was successful in significantly reducing inhibitor costs in the field as well as in increasing confidence in inhibitor performance and better understanding of how the inhibitors work in the system. It was also successful in proving electrochemical noise is a viable option for field inhibition testing and that by using electrochemical noise it is possible to obtain complete inhibitor testing in the field in a very short period of time compared to traditional testing methods
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".