Mapping Effective Corrosion Inhibitor Dose in a Large Onshore Oilfield
Bibliographic record
Abstract
Abstract Surfactant-based corrosion inhibitors are widely used in oilfield production systems. Ensuring that an appropriate dosage of inhibitor is present throughout a variable production network is very challenging and can present a serious risk in asset integrity. Using micelle detection for diagnosis of the presence of an adequate inhibitor dose has been demonstrated as a method which avoids some of the difficulties and potential inaccuracies of residual measurement whilst still providing a rapid measurement of functional dose content. In this study it was applied to the analysis of spot samples taken across a large onshore production system encompassing three different corrosion inhibitors in two nearby fields and a water injection system serving both. The results were quite different across the three systems. The larger onshore system was found to contain micelles in very few samples and showed that more performance could be sought by increasing dosage, the smaller production system contained micelles throughout indicating the possibility of decreasing dosage if required and the water injection system showed a depletion of micelles across the length of the system with some sub-optimal dosage towards the terminus.
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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 | 0.000 |
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".