On the Use of Particle Size Distribution Data for Permeability Modeling
Bibliographic record
Abstract
Abstract 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 sediment's nature. 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 to depositional environment, including lithological association, stratigraphy, aerial distribution, and associated physical processes. Particle size distribution is known to be a significant factor for evaluating bitumen recovery from an oil sands mine. This is because presence of fines (evaluated by PSD analysis) affects the hot water separation process, processing plant recovery prediction and provides grade control. Presence of more fines translates into lower recovery from commercial oil sands processing. In this paper we investigate whether the particle size distribution should be also considered a critical parameter for evaluation and estimation of permeability of an oil sands reservoir. We show using the data from the Cenovus Energy's Telephone Lake lease that there is a strong relationship between permeability and particle size distribution data. We also show that the information provided by the PSDs for permeability prediction is more significant than the one inferred from a simple porosity-permeability relationship. Subsequently, we comment on permeability modeling using particle size distribution data and list the techniques available for cleaning and modeling of multivariate PSDs. We document a methodology for accurate modeling of PSDs and provide a workflow 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".