Experimental Evaluation of SAGD/ISC Hybrid Recovery Method
Bibliographic record
Abstract
Summary Most current in-situ recovery methods have limitations which restrict their application in many heavy-oil reservoirs. There is also a variety of undesirable environmental issues associated with existing thermal-recovery techniques. To overcome some of these problems, novel hybrid processes have been proposed that combine steam-assisted gravity drainage (SAGD) and in-situ-combustion (ISC) technologies. In the conventional ISC process, oil is displaced by an elevated-temperature combustion zone, but when the native oil mobility is low, this displacement becomes problematic because of the low oil mobility downstream of the combustion front. Heating the reservoir and reducing the distance between injector and producer can prevent such cold liquid blocking in the combustion process. This suggests that a well configuration that greatly decreases the distance between the injector and the producer (e.g., the SAGD well configuration) could be advantageous in ISC-based recovery methods. Moreover, running the SAGD operation in the initial period creates a vapour-filled depleted zone in the reservoir that would be sufficiently hot to start combustion on air injection. To investigate the feasibility of this idea, a hybrid experiment was conducted at a representative reservoir pressure. During the experiment, SAGD was operated in a physical model for a period and then combustion was started by switching from steam injection to air injection. A modified well arrangement was used to help the process operation, oil ignition, and combustion-front formation. Residual oil and water saturations in the model were analyzed. Also, core samples were extracted from the model to evaluate asphaltenes and coke formation. It was shown that it may be possible to recover nearly 70% of the original oil in place with a modified well arrangement.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".