Experimental and Mechanism Study of Superheated SAGD vs. Conventional SAGD Technique: A Cost-Effective Scheme for Superheated SAGD
Bibliographic record
Abstract
Steam-assisted gravity drainage (SAGD) is one of the steam injection techniques to exploit heavy oil and extra heavy oil resources, where the nature of steam is crucial to the production efficiency. Replacing saturated steam with superheated steam can effectively improve the steam quality at the bottom of the well and the production efficiency. In this study, based on the 2-D SAGD experiments, the recovery mechanisms of SAGD under the 220°C saturated steam and 260°C (superheated degree of 40°C) and 300°C (superheated degree of 80°C) superheated steam are compared and analyzed. The numerical model was developed based on experimental results, and the influence of steam superheated degree on the recovery degree of the SAGD process was further investigated. The physical experiment results and numerical simulation results show that the advantages of high enthalpy and large specific volume of superheated steam are significant at the horizontal expansion stage of the steam chamber stage compared to those of saturated steam. However, although the superheated steam can improve the recovery degree, the economic efficiency may decrease with the addition of superheated steam since it requires higher energy to generate the superheated steam. Thus, the SOR (steam-oil ratio) cannot appropriately describe the energy and economic efficiency when superheated steam is considered. Therefore, the cumulative FOR (fuel-oil ratio) is proposed, and the optimal superheated degree, optimal injection strategy, and its relation with the recovery mechanisms are studied. The results indicate that using superheated steam at 80°C superheated degree during the steam chamber horizontal expansion stage can increase the recovery factor around 12% and also reduce the cumulative FOR around 5.3 compared to the conventional SAGD strategy.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".