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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".