Modeling Secondary Oil Migration with Core-Scale Data: Viking Formation, Alberta Basin
Bibliographic record
Abstract
Abstract The Viking Formation in the Alberta basin contains approximately 88.7 x 106 m3 (5.579 x 108 bbl) of recoverable oil, which migrated more than 200 km, as indicated by oil-source rock correlation. Simulating the mechanisms controlling secondary oil migration (hydrodynamics, buoyancy, and permeability heterogeneity) is beneficial for exploration, but it remains extremely difficult to predict oil occurrences. Although core-scale petrophysical data for the Viking Formation are abundant (> 69,000 core plugs), the extent of fracture permeability and permeability alteration due to diagenesis are unknown. Moreover, sampling bias may affect the permeability distribution in unpredictable ways. Numerical simulations of oil migration were conducted using the highest core-plug measurement of permeability from each borehole to obtain an upper bound on oil migration velocities. This permeability model is not appropriate for simulating stratigraphic entrapment of oil, but it does reveal that core-scale data are in the appropriate range of magnitude to have allowed significant oil migration. Regional groundwater flow was essential for charging several of the largest and most distant oil fields in the Viking Formation. Maximum core-plug permeability data are useful for modeling the extent of secondary oil migration and may have applications to fluid flow and transport modeling in other foreland settings.
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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 | 0.001 |
| 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".