Experimental investigation on the SAGD dilation start-up in shallow heavy oil reservoirs
Bibliographic record
Abstract
Abstract There are few related researches focused on the steam assisted gravity drainage (SAGD) dilation start-up technology (termed as dilation start-up in this paper), especially with physical modelling experiments. Therefore, in order to better understand the dilation start-up process, several large-scale experiments of SAGD start-up with dual horizontal wells were carried out by using the oil sands from Xinjiang oilfield, northwest China. The dilation process characterized by temperature changes at different positions inside the experimental samples was monitored in real time. The performance differences between dilation start-up and conventional start-up were discussed in detail, and the effects of dilation pressure and dilation time on the dilation process were also studied. The experimental research indicated that dilation start-up can significantly enhance the range of dilation zone and improve the uniformity of dilation zone along the horizontal wellbores. Moreover, it was found that dilation pressure is an essential factor influencing the dilation effect, in that the size and distribution of dilation zone are largely dependent on dilation pressure. The evolution of dilation zone with time shows that a long-time dilation prompts the uniform propagation of dilation zone along the horizontal wellbores.
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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".