Overview of Performance and Analytical Modeling Techniques for Electromagnetic Heating and Applications to Steam-Assisted-Gravity-Drainage Process Startup
Bibliographic record
Abstract
Summary Steam-assisted gravity drainage is the method of choice to extract bitumen from Athabasca oil-sand reservoirs in Western Canada. Under reservoir conditions, bitumen is immobile because of high viscosity, and its typically high level of saturation limits the injectivity of steam. In current industry practice, steam is circulated within injection and production wells. Operators keep the steam circulating until mobile bitumen breaks through the producer and communication is established between the injector and the producer. The “startup” phase is a time-consuming process taking three or more months with no oil production. A variety of processes could be used to minimize the length of the startup phase, such as electromagnetic (EM) heating in either the induction (medium frequency) or radio-frequency ranges. Knowledge of the size of the hot zone formed by steam circulation and of the benefits of simultaneous EM-heating techniques increases understanding of the startup process and helps to minimize startup duration. The aim of the present work is to introduce an analytical model to predict startup duration for steam circulation with and without EM heating. Results reveal that resistive (electrothermal) heating with/without brine injection cannot be a preferable method for mobilizing the bitumen in startup phase. Induction slightly decreases startup time at frequencies smaller than 10 kHz, and at 100 kHz it can reduce startup time to less than two months.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".