Poroelastic Effects of Cyclic Steam Stimulation in the Cold Lake Reservoir
Bibliographic record
Abstract
Abstract The heating of bitumen reservoirs by cyclic steam stimulation (CSS) requires high pressures and temperatures that disturb the soil matrix. This disturbance results in a dilation of the soil matrix creating regions of enhanced permeability and porosity impacting both the injection and production cycles of steam stimulation as well as affecting porous zones not hydraulically connected to the reservoir. The volumetric strain changes reach far beyond the region of injection and include areas of both expansion and compression. The magnitude of the volumetric strains may result in significant displacements and strains being transferred to porous zones outside the reservoir. This can result in observable pressure changes in an otherwise hydraulically static system. Pressure data at an observation well drilled to monitor aquifer pressures showed a significant response to the CSS process occurring in the reservoir about 300 m below the aquifer. Coupled geomechanical and reservoir modeling and an analytical calculation both show that this pressure behavior is attributed to the poroelastic response of the aquifer due to the CSS process occurring in the reservoir. This paper describes the field data, the methodology of the coupled reservoir and geomechanical modeling, and the result of the analysis of the field data with the coupled model. The analysis shows clearly that the aquifer response is not due to a hydraulic communication with the reservoir. It also shows the capabilities of coupled modeling, as this type of a problem could not be analyzed with conventional reservoir simulation tools.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".