A Condensation Temperature Model for Evaluating Early-period SAGD Performance
Bibliographic record
Abstract
Abstract This paper proposes a new methodology using condensation model to evaluate the early-period SAGD by interpreting the temperature falloff data in injector or producer obtained from fiber optics or thermal couples after the wells are shut-in. Based on the non-condensation model proposed before, the condensation model also assumes a circular hot-zone shape since in the early stage of SAGD operation, and characterize the system as composed of a steam-zone of steam temperature, a cold-zone of reservoir temperature and a transition-zone in between as the initial temperature distribution. Besides, the condensation model incorporates the effect of steam condensation on the condensation-front. The movement of steam condensation-front is calculated to account for the steam-zone shrinkage. Sensitivity analysis over this models indicates that the sizes of steam-zone, transition-zone and the observing location directly affect the temperature behavior at observation point. Synthetic case study shows that the temperature falloffs from condensation model and from simulation are in good agreement and suggests that condensation model can be used to estimate the chamber size at the early stage of SAGD. As is known, it is important to obtain an even steam chamber distribution along the horizontal wellbore to shorten the ramp-up time so that maximized economics can be achieved. In reality, the reservoir heterogeneity, the wellbore undulation and the operation condition make the steam chamber conformance impossible. Because of the ready-to-use temperature data and the semi-analytic solution, the condensation model proposed in this paper can provide quick and reliable estimation of the steam chamber size to help the engineers to monitor and optimize the chamber development thereafter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".