Investigation of the performance of low-dosage hydrate inhibitors combined with wax inhibitors for a mixed CH<sub>4</sub>-CO<sub>2</sub> gas hydrate formation
Bibliographic record
Abstract
Within the oil and gas industry, low-dosage hydrate inhibitors (LDHIs) are a proven technology to control hydrates. Besides hydrate inhibitors, wax inhibitors (WIs) are frequently injected to prevent wax buildup in the crude oil pipeline. However, little attention has been focused on the effect of wax inhibitors on the performance of LDHIs. In this study, performance tests of 3 LDHIs in the presence of wax inhibitors were carried out for a 67% CH4/33% CO2 gas hydrate formation. Using the isothermal cooling method at pressures of 9 MPa and temperatures of 4 °C (subcooling is 9 °C), the results showed that the induction time of CH4-CO2 gas hydrate formation with LDHI/WI was shorter than the system with only LDHI. During the growth period, when the concentration of the WIs was 1 mass%, the growth time of the system with LDHI/WI was prolonged. Taking the induction time and the growth time into consideration, it was found that WIs had a more negative impact on the kinetic hydrate inhibitor performance at low dosage. The effect of WIs at high concentration could be negligible.
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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".