Obtaining Batch Corrosion Inhibitor Film Thickness Measurements Using an Optical Profiler
Bibliographic record
Abstract
Abstract Batch corrosion inhibitors are widely used for the corrosion control of production wells and pipelines in the oil and gas industry. Rules of thumb that incorporate film thickness, contact time and surface area are still commonly used to calculate the volume of batch inhibitor required for pipeline applications. Measuring the actual thickness of the inhibitor film on the metal and the impact of different variables on the film (e.g. inhibitor type, contact time, diluent type, dilution ratio, shear stress) offer the potential to provide a better understanding for optimizing the application procedure and required batch frequency. Optical profiling (or white light interferometry) has long been a standard technique for non-contact, 3D measurement of surface topography. This method has now been extended to thickness measurements of semi-transparent batch inhibitor films. In this paper, the impact of several variables (i.e. inhibitor type, contact time, diluent ratio, shear stress) on batch inhibitor films was studied using an optical profiler to advance our knowledge of batch inhibitor application techniques and to optimize batch programs (e.g. film persistency, batch frequency).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".