Bibliographic record
Abstract
Summary Predicting the performance of in-situ recovery processes in the McMurray formation is required to optimize development planning and resource management. These performance predictions are sensitive to many parameters; however, vertical permeability is perhaps the most critical geological parameter. There are many challenges associated with the estimation of vertical permeability: (1) it is difficult to collect representative core measurements, (2) the high viscosity of the bitumen makes it impossible to perform well testing, (3) statistical approaches and the notion of representative elementary volumes (REVs) are challenged by heterogeneities at all scales and (4) the nature of the heterogeneities is variable within different depositional environments. This paper summarizes these challenges, then presents a consistent numerical modelling framework based on core data, core photographs, conventional well-logs, high-resolution image logs and detailed geological interpretation. The framework includes: dividing the stratigraphic column into facies with similar spatial arrangement of sand/shale, constructing high-resolution models of sand/shale, assigning porosity and permeability to sand/shale, calibrating the models to direct measurements, solving for effective horizontal and vertical permeability at the appropriate scale and transferring the results to geomodelling. This framework is described in detail and demonstrated with illustrative examples. Considerations for even better results are discussed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".