A Mathematical Improvement to SAGD Using Geomechanical Modelling
Bibliographic record
Abstract
Summary Steam assisted gravity drainage (SAGD) is a thermal oil recovery technique which has been used mostly for Alberta's unconventional oil sands reservoirs. Roger Butler, known as the father of SAGD, was the first one to establish a theory and an analytical model for SAGD. His model is a rigorous solution and is widely referred to as a SAGD fast flow simulator. However, geomechanics, which has been shown to be a relevant part of SAGD's physics, has not been included in the model. When rock properties are influenced by geomechanical behaviour, the Butler theory is not able to capture the complete physics of the SAGD process. In such cases, the model must adopt unrealistic or high values for rock properties. In this study, a classical theory in the field of geotechnical engineering (limit equilibrium) is employed to act as the geomechanical module for SAGD's mathematical coupled simulation. The Butler/Reis model has also been improved using a model of slices for flow simulation. Methodology of combining these two models in a single coupled mathematical simulator is presented in this paper. The solver is a fast and realistic proxy and can be used as a low-order tool for history matching. The results of coupled simulations show that the model is able to predict permeability and porosity of the reservoir closer to real values than uncoupled (flow only) modelling.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".