Experimental Evaluation of SAGD-ISC Hybrid Recovery Method
Bibliographic record
Abstract
Abstract Most of the current in-situ recovery methods have limitations which restrict their application in many heavy oil reservoirs. There are also a variety of undesirable environmental costs associated with the existing recovery techniques that limit their desirability. To overcome some of these issues, recent novel hybrid technologies have been proposed based on combining Steam Assisted Gravity Drainage and In Situ Combustion technologies. In the conventional ISC process, oil is displaced by an elevated temperature combustion zone but when the native oil mobility is not high enough this displacement becomes problematic. Warming up the reservoir and reducing the distance between injector and producer is needed to prevent liquid blocking inside the reservoir during the combustion process. This suggests that a well configuration that greatly decreases the distance between the injector and the producer would be advantageous in ISC based recovery methods and the well configuration used in SAGD comes to mind. Running SAGD in the initial periods creates a vapor-filled depleted zone in the reservoir which is hot enough to start combustion on air injection. To investigate the possibility of this idea, a hybrid experiment was conducted at a representative reservoir pressure. During the experiment, SAGD was operated in the model for a period and then combustion was started by switching from steam injection to air injection. A well arrangement was considered to help 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 concluded that it is possible to recover almost 70% of the original oil in place with a special kind of 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".