The Analysis of Hydrates Frozen Blocking in Fire Flooding Exhaust Gas of Heavy Oil
Bibliographic record
Abstract
The No. 56 desulfurization tower pipeline network of Dawn production plant in Liaohe Oilfield is fire flooding exhaust gas collecting pipe. Since put into production, because of the large amount of gas and liquid, fine pipelines and big quantities of bends, the frozen blocking often occurs in winter. The site only adds methanol inhibitors on the basis of production experience, although the frozen blocking phenomenon is eased, it also shows frozen blocking phenomenon in the well interchanges and ups and downs. Now aiming at the problem, based on the percentage of hydrate cross-sectional area, through the establishment of exhaust gas pipe network model and mathematical model, we analyzed how the various factors (temperature, pressure, ground temperature, throughput) influenced the frozen blocking of pipe networks, and the results showed that the cross-sectional area percentage of 37.6% is frozen blocking break points, and the input’s effect on the frozen blocking is the largest. The error in this mathematical model between the prediction of frozen blocking position and actual position is within the scope of the permit (5%). So it can guide the production work in the winter, to reduce the loss of oil field, and increase the economic benefit. Key words: Fire flooding exhaust; Frozen blocking; Mathematical model; Frozen blocking prediction
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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".