STATUS AND DEVELOPMENT TREND OF HEAVY OIL COLD PRODUCTION TECHNOLOGY OF THE WORLD
Bibliographic record
Abstract
This paper reviews the status of heavy oil cold production technology of the world,and discusses its development trend in the future.Price decontrol and global drop in oil price in 1986 drove down the cost effectiveness of thermal recovery and heavy oil production in general.Since that time,some small oil companies in Canada started to keep on probing heavy oil cold production technology in fields and gained unexpected success.Up to the mid 90s,cold production with sand had become a hot technical spot for heavy oil development.Besides small oil companies,many large oil companies also set their foot in this field.Some related resarch organizations and institutions had devoted themselves to the study of the mechanism of cold production with sand,putting forward the theory of wormhole and foamy oil.ln 1996,in order to study and extend the heavy oil cold production technology in China,CNPC put it as a key research project of the Ninth-Fivenational development plan,organizing a series of feasibility study and field tests in Henan oilfield.Aladeiba and Bentiu reservoirs of the Fula oilfield in block 6,Sudan are typical unconsolidated heavy oil formations,easy at producing sand,with low pressure.To efficiently develop such reservoirs, CNODC conducted a series of investigation and field tests last year and believed that the technology of cold production with sand is the prospecting one for developing the Fula oilfield.At present,there still exist some problems in aspects of the recognition for cold production mechanism and of the improvement for production technologies.For the future,RD for cold production technology will focus on four hotspots.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".