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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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) | 0.000 | 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 teacher head, 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".