Bibliographic record
Abstract
Abstract SAGD is an energy and emissions intensive process for bitumen recovery. The situation is worse for reservoirs with lean zones. Industry is looking for ways to make these projects more environmentally friendly and economic by reducing energy intensity and emissions. Previously we have shown that for reservoirs with top water, SAGD is a viable option. Active de-watering of the lean zone can be accomplished using fence wells and air injection, though some water would remain. A new process is presented in which vaporized solvent is used in such a way that it helps keep water ingression into the drainage chamber at bay. The process is initiated as conventional SAGD. Once steam chamber breaches the de-watered lean zone, one of the previously drilled wells for de-watering is converted to solvent injection while steam injection is ceased. Solvent is injected as vapor at such a rate that it forms a gas blanket over the steam chamber and condenses further away due to cooler temperature in the lean zone. The combined effect of solvent injection pressure and an oil bank formedat the edges of the solvent blanket keep the water at bay. Within core of the vapor chamber, solvent vapor makes contact with cold bitumen, dissolves in the oleic phase and reduces its viscosity. The mobilized oil moves down to the producing well by gravity drainage. Simulation results show that using propane as the solvent, energy consumption of the process will be reduced by 80% as compared to SAGD (which is still being optimized via simulations), while the average production rate is doubled. Past the SAGD phase, since no steam is injected and the condensed solvent bank is effective in blocking the lean zone water, produced water to oil ratio is very low. Once economic recovery is achieved, propane retained in the reservoir can be recovered by a variety of ways that are discussed in the paper. The process described above can reduce the capital cost of a green field project by minimizing the water handling facility and significantly reduce the carbon footprint.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".