Microcarriers for Smart Release of Kinetic Hydrate Inhibitors Under Hydrate Formation Condition Inside Flowlines
Bibliographic record
Abstract
Abstract Hydrate formation in subsea flowlines is one of the main concerns to safely transport the hydrocarbon fluids in the field of flow assurance. To prevent blockage of hydrate, thermodynamic hydrate inhibitors (THIs) have been injected into the subsea flowlines; however, the high injection amount of THIs may drive up operational expenditure (OPEX) with increasing water depth of oil and gas reservoirs. Kinetic hydrate inhibitors (KHIs) have been investigated as an alternative for few decades. In this study, microcapsules were designed using microfluidics concept for efficient release of KHIs at off-shore transport line of hydrocarbon and gas. Hydrate formation kinetics among four different systems, pure water, microcapsules containing poly vinyl caprolactam (PVCap), microcapsules without PVCap, and PVCap aqueous solution was investigated. The dispersive Raman was applied to understand hydrate formation on microcapsules with temperature change under static condition, and autoclave study also was employed to confirm the shear rate effect on microcapsule stability. From the both experiments, it was confirmed that release of KHI solution from microcapsules delayed the formation of hydrate at the target temperature and showed a similar efficacy comparing to the bulk KHI solution under turbulent condition. These results indicated the release of PVCap solution can be controlled by adjusting the membrane thickness of the microcarriers, which would provide operational flexibility to manage the risk of hydrate plug formation.
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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) | 0.000 | 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 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".