Evaluation of Anti-fouling Surfaces for Prevention of Mineral Scaling in Sub-surface Safety Valves
Bibliographic record
Abstract
Abstract Mineral scale formation and deposition in down-hole completion equipment such as subsurface safety valves can cause dramatic and unacceptable safety risks and associated production losses and operational costs. Current scale removal strategies involve both mechanical and chemical technologies, each of them having their own advantages depending on the type of mineral scale and its location. However, these techniques are often costly and of limited efficiency. The current study assesses the ability of a range of chemically and morphologically modified coatings to prevent/reduce mineral scale surface fouling. Building-up on previous work done under static conditions, this paper presents results from scaling tests under laminar and turbulent dynamic conditions using a rotating cylinder electrode under in a complex (mixed) scaling environment (supersaturated w.r.t. calcium carbonate, barium sulfate, strontium sulfate, barium carbonate and strontium carbonate). The study shows that if properly selected, surface treatments represent a promising approach to reduce scale deposition on downhole equipment surfaces that are critical to maintain equipment functionality and thereby well safety barrier integrity. By analyzing the scaling behaviors observed within the set of surfaces tested, suggestions of the controlling factors in anti-fouling on these systems are presented and discussed.
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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 | 0.001 |
| 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 | 0.000 | 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".