On the Use of Particle-Size-Distribution Data for Permeability Prediction
Bibliographic record
Abstract
Summary Particle-size distribution (PSD) is a list of values that defines the relative amount of particles present according to the size in a sample. The PSD of the McMurray Formation sediments characterizes rock granulometry and is a fundamental indicator of the nature of the sediment. The size distribution of the component solid particles in the McMurray Formation sediments relates to their porosity; volume of water and bitumen contained within the pore space; and the depositional environment, including lithological association, stratigraphy, areal distribution, and associated physical processes. PSD is known to be a significant factor for evaluating bitumen recovery from an oil-sand mine. This is because presence of fines (evaluated by PSD analysis) affects the hot-water-separation process and processing-plant recovery prediction and provides grade control. Presence of more fines translates into lower recovery from commercial oil-sand processing. In this study, we investigate whether the PSD should be also considered a critical parameter for evaluation and estimation of permeability of an oil-sand reservoir. We show, by use of the data from Cenovus Energy's Telephone Lake lease, that there is a strong relationship between permeability and PSD data. We also show that the information provided by the PSDs for permeability prediction is more significant than that inferred from a simple porosity/permeability relationship. Subsequently, we comment on permeability modeling by use of PSD data and list the techniques available for cleaning and modeling of multivariate PSDs. We document a methodology for modeling of PSDs and provide a work flow for incorporating these data in improved understanding and modeling of permeability and its distribution.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".