Bibliographic record
Abstract
Abstract This paper discusses the recent patent application filed by EnCana Corporation on the use of air injection to improve the performance of steam assisted gravity drainage (SAGD). EnCana's SAGD/Air Injection process employs standard SAGD well-pair infrastructure. It optimizes between the ability of steam to preheat the reservoir during SAGD and the superior (follow-up) oil displacement efficiency of in-situ combustion. Operationally, air injection is initiated after thermal communication has been established between well-pairs with steam. One interesting feature of this operating strategy is that down-hole bulk separation of oil and gas occurs which facilitates (a) efficient monitoring and control of the combustion, (b) design of surface facilities, and (c) corrosion mitigation. Laboratory combustion tube tests are presented that confirm the ability to initiate and sustain combustion, as well as mobilize residual oil saturation to steam, within a SAGD chamber. These experiments were initialized at oil saturations and conditions representative of those in a steam chamber. The residual oil saturations were determined from a full-hole core taken in the vicinity of a mature SAGD well-pair at Foster Creek. Numerical simulations of post-SAGD air injection are presented that suggest the ability to displace and produce oil banks between well-pairs and that recovery factor can be increased up to 8% of the original oil-in-place over conventional SAGD. The simulations show oil production rates and recovery factor are expected to increase with higher air injection rates. However, instantaneous air-oil ratios, which are indicative of operating costs, also increase. Thus there is an optimum continuous air injection rate that maximizes profitability. Simulations further indicate that it is possible to recycle flue gases in the injection stream without affecting oil recovery.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".