Anomalous surface heave induced by enhanced oil recovery in northern Alberta: InSAR observations and numerical modeling
Bibliographic record
Abstract
Abstract Recent interferometric synthetic aperture radar observations over northern Alberta, Canada, show persistent surface heave occurring at rates of 1–4 cm/yr, localized at sites where the steam‐assisted gravity drainage technique is currently used to extract bitumen from the Athabasca oil sands. We find that uplift rates above the horizontal injector wells are strongly correlated with rates of steam injection, even though there is a net fluid loss from the reservoir pore space as oil and water are withdrawn through the production wells. In combination with available steam injection and bitumen production data at four sites, we use reservoir flow models to explain how the thermal and geomechanical effects of steam injection on an oil sand reservoir can generate uplift at the surface. Results of our numerical experiments show that persistent surface heave consistent with observed rates can be driven by stress changes in the reservoir due to porous flow and thermal expansion. We also observe an unexpected localized uplift, of magnitude equal to or greater than the heave above the sites of steaming but located at clusters of wellheads which are outside the region of influence of the steam chambers. We show that this “wellhead” deformation can be explained by thermal expansion of rock near the injector wells.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".