Effectiveness of Low-Dosage Hydrate Inhibitors and their Rheological Behavior for Gas Condensate/Water Systems
Bibliographic record
Abstract
Management of hydrate flow assurance issues has been a major problem in offshore and deepwater–oil and gas production. Hydrate flow assurance strategies include the use of low dosage hydrate inhibitors (LDHIs) such as kinetic hydrate inhibitors (KHIs) and antiagglomerants (AAs) among others. The effectiveness of AAs should be studied in detail before embarking on using such chemicals in the field. In the current work, a systematic AA laboratory screening study has been undertaken on a gas condensate field. The rheological measurement of hydrate slurry in the presence of AAs under various operating conditions provided important insight into AA screening. Hydrate slurries were formed in a fully visual sapphire pressure–volume–temperature (PVT) cell in the presence of various AA chemicals. The hydrate slurry was then transferred to a high pressure rheometer for viscosity measurements at various shear rates. The effectiveness of these chemicals and their rheological behavior were evaluated as a function of pressure, water cut, and shear rates under various operating scenarios, that is, flowing conditions and shutdown/restart conditions. The result of this hydrate flow assurance testing indicated that hydrate slurry in the presence of the AAs behaved as a non-Newtonian fluid with a shear thinning effect. The water cut and pressure had an impact on the effectiveness of the AAs.
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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".