Numerical Modelling of Geomechanical Effects During Steam Injection in Sagd Heavy Oil Recovery
Bibliographic record
Abstract
Abstract Steam Assisted Gravity Drainage (SAGD) is a thermal process that has found wide application in high permeability heavy oil or bitumen reservoirs, mainly in the Western part of Canada. In this process, steam injection continuously modifies reservoir pore pressure and temperature, which can change the effective stress in the reservoir, resulting in a complex interaction of geomechanical effects and multiphase flow in the cohesionless porous media. Quantification of the state of deformation and stress in the reservoir is therefore essential for the correct prediction of reservoir productivity but also for the interpretation of 4D seismics used to follow the development of the steam chamber. On another side, this quantification is crucial for the evaluation of surface uplift, risk of loss of seal integrity, hydro fracturing and well failure. Simultaneous study and analysis of interrelated geomechanics and fluid flow in the reservoir are thus crucial for the management of the process at different stages. The objective of this paper is to show the importance of taking into account the role of geomechanics in the numerical modelling of SAGD and to provide a better description of the rock contribution to fluid flow in this process. A geomechanics- reservoir partially coupled approach is presented that allows to iteratively take the impact of geomechanics into account in the fluid flow calculations and therefore performs a better prediction of the process. The proposed approach is illustrated on a realistic field case.
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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 | 0.001 | 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) | 0.001 | 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".