Rigorous coupling of geomechanics and thermal-compositional flow for SAGD and ES-SAGD operations
Bibliographic record
Abstract
Abstract Steam Assistant Gravity Drainage (SAGD) is widely used to recover heavy oil and bitumen reservoirs. Typical SAGD operations involve a pair of horizontal wells separated vertically. Steam, or a steam-solvent mixture (e.g., Expanding- Solvent SAGD), is injected into the upper well to form a steam chamber and mobilize oil, which drains to the lower production well. Significant mechanical stresses associated with this process can increase the risk of fracturing the reservoir, or cap-rock. We perform a fully-coupled thermal-compositional-mechanical numerical simulation of SAGD and ES-SAGD processes for a typical bitumen reservoir in the Fort McMurray region of Alberta, Canada. A mixed finite-volume approximation for the flow and a Galerkin finite-element approximation for the mechanics are used, and the resulting set of nonlinear equations are solved using a fully implicit formulation. The two discretizations share the same unstructured grid. We demonstrate that thermo-mechanical effects can be quite significant in SAGD operations. The sharper gradients associated with the standard SAGD process increase the risk of damaging of the cap-rock. On the other hand, ES-SAGD operations lead to more dispersed temperature and pressure distributions, which decreases the possibility of damaging the cap-rock.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".