Numerical Assessment of Caprock Integrity in SAGD Operations
Bibliographic record
Abstract
Abstract This paper focuses on investigating the impact of Steam Assisted Gravity Drainage (SAGD) operations on caprock stresses. Steam injection, in SAGD operations, results in significant pore pressure, temperature, stress and volumetric changes in the reservoir. One outcome is the deformation and stress variations of the overburden strata that could lead to the containment breach of the caprock through shear or tensile failure. We developed a model based on the nucleus-of-strain method to assess the reservoir volume changes caused by the combination of reservoir shear dilation, thermal expansion, and increased pore pressure during the SAGD operations. We then developed a forward geomechanical model and used the reservoir volume changes as input data. The nucleus-of-strain model was verified by comparing the measured surface heave data with the same from the forward model. The geomechanical model was used along with the Mohr-Coulomb failure criterion to assess the stresses and failure potential of the overburden strata. The model was applied to a SAGD reservoir for which surface heave data were available. The outcome indicates the potential for caprock integrity problems due to tensile fracturing. A parametric study was also performed to investigate the sensitivity of the predictions to various significant parameters including the Young's modulus, Poisson's ratio, in situ stress gradient, and reservoir depth, among others. The outcome of the parametric studies will be discussed in this paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".