An Exact Downscaling Methodology in Presence of Heterogeneity: Application to the Athabasca Oil Sands
Bibliographic record
Abstract
Abstract Geostatistical realizations are often built at an arbitrary scale based on available data and computational resources. In certain settings, it may be necessary to downscale the realizations for flow simulation and local resource assessment. This is especially important in the Athabasca Oilsands where accurate flow simulation often requires numerical models with a very fine grid size. Flow simulation is undertaken for selected areas and realizations. It is intractable to construct the original geostatistical models at the fine scale. It is desirable to construct finer scale models that reproduce the original realizations exactly. Approximate downscaling is always possible with geostatistical methods; however, it is of interest to create fine scale models that exactly reproduce the large scale models to ensure consistency and avoid potential biases. Direct block sequential simulation is developed to generate fine scale realizations that exactly reproduce block data. A comprehensive case study is shown from the Athabasca Oilsands. Geostatistical realizations are constructed over 100s of square kilometers at a large scale. These realizations are locally downscaled to 20m by 2m by 2m for flow simulation around particular SAGD well pairs. The fine scale realizations are constructed such that they exactly match the initial coarse scale realizations. An approximate downscaling method is also used. The 3-D models and flow simulation results were compared to show the difference made by the exact downscaling method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".