Making Sense of the Geomechanical Impact on the Heavy-Oil Extraction Process at Peace River Based on Quantitative Analysis and Modeling
Bibliographic record
Abstract
Abstract The steam injection rates in the CSS operation for the extraction of the Peace River bitumen can be significantly increased by operating at a pressure above the vertical stress of 13 MPa. To improve the understanding of the CSS extraction process, Shell Canada designed and implemented a monitoring program over the most recently drilled production pads. This program included microseismic, surface time-lapse seismic (2D and sparse 3D), a time-lapse 3D VSP, a surface tiltmeter array, and InSAR. Joint interpretation of these data with production data has allowed us to build a conceptual model of the geomechanical response of the reservoir and its effect on the production process. Dynamic reservoir simulations for Pad 40 were done with the aim to obtain a predictive model. A dilation model from previous simulation work for Cold Lake CSS was applied on the basis of the monitoring analysis and incorporated into the simulations together with a relative-permeability-hysteresis model. A good match of the injection and production volumes, and injection wellhead pressures for the early cycles was achieved using a single well model. Simulation of the later cycles requires a full pad dynamic model constraint by monitoring data, if the heterogeneous steam distribution suggested by the monitoring data becomes significant.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".