Forward Modeling of SAGD-Induced Heave and Caprock Deformation Analysis
Bibliographic record
Abstract
Abstract This study focuses on the investigation of the mechanism behind surface heave induced by thermal recovery. Injection of steam into oilsand reservoirs results in changes in temperature and pore pressure within the subsurface. These changes can induce a reduction of effective stress, which provides initial containment of bitumen. Irreversible shear/tensile deformation of the caprock and overburden might be one of the unwanted consequences of such process. We developed a work flow incorporating the Finite Difference Method (FDM) and Finite Element Method (FEM). Heat and fluid flows are computed using FDM with CMG-STARS and elastic/plastic deformation estimations are done through Abaqus FEM simulation software. The Mohr-Coulomb yield criterion and the non-associated flow rule is used to assess the plastic failure in the reservoir and caprock. This work flow is used to assess the impact of the SAGD process on the integrity of caprock. The geomechanical model is calibrated with surface deformation measurements. Parameter search is performed to study how geomechanical parameters influence the expression of reservoir deformation at the surface. The uncertainty associated with the modeled results shows the need for a more refined geomechanical model, considering the heterogeneous nature of geological structure in the region of interests.
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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 | 0.000 |
| 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 | 0.001 |
| 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".