Coupled Geomechanical and Thermal Simulation of SAGD Process
Bibliographic record
Abstract
Abstract SAGD (steam assisted gravity drainage) is a coupled geomechanical problem because continuous steam injection changes reservoir pore pressure and temperature, which can alter the effective stress in-situ, resulting in the dynamic changing of porosity and permeability. A conventional modeling approach remains difficult in capturing the Flow-Stress interactions, as well as in modeling surface deformation during injection and production. Therefore, to optimize SAGD operating strategy and minimize the environmental risk, a coupled geomechanical simulation that solves the flow and stress equations simultaneously is the solution. In addition, thief zone SAGD performance is very sensitive to the high injection pressure, which may cause the great heat loss due to steam breakthrough into thief zones. Flow-stress coupling (STARS+Geomechanics) using a modular coupled approach is investigated in this paper. The iterative coupling between the thermal simulator and stress model has the advantage of flexibility and computational efficiency, where pressure and temperature changes occurring in the reservoir simulator are passed to the geomechanical simulator to compute the changing of stress and strain, and update porosity and permeability simultaneously. A generalized hyperbolic constitutive model for oil sands that approximates the dominant mechanism is implemented; the failure envelope is defined by a Mohr-Coulomb relation controlled by the friction angle. Coupled geomechanical simulation results demonstrate the permeability enahncement and surface heave, caused by dilation and thermal expansion, associated with the steam injection. A control volume and K-value based multiphase multicomponent thermal simulator (FATS) is utilized aiming to investigate the detrimental effect of thief zones (top water and top gas). Results have been validated by STARS, demonstrating that the steam chamber has been shifted into thief zones after steam breakthrough under high injection pressure.
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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 | 0.001 |
| 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".