Practical Considerations of Reservoir Heterogeneities on SAGD Projects
Why this work is in the frame
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Bibliographic record
Abstract
Abstract SAGD has been implemented through a variety of different operating schemes. Determining the optimum and cost-effective strategy poses the greatest challenge to any SAGD reservoir. In addition to average rock quality, reservoir heterogeneities have a deep impact in steam chamber development and the overall volumetric sweep. This paper examines two well pairs in the Surmont pilot project. From this analysis and general experience, the following critical factors must be considered when optimizing SAGD operating strategies: Getting enough heat close to the producer Accounting for areal (lateral) heterogeneity Identifying shale extent and its effect in partially retarding steam chamber height growth Determining the effect of water saturation and assumed irreducible water saturation Understanding that permeability is often higher than air permeability most likely due to dilation effects This paper also addresses potential geological risk through analogy and the amount of heterogeneity that must be accounted for when developing a representative simulation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it