Classification of Alberta Oil Sands Based on Particle Size Distribution for Sand Control Design and Experimental Applications
Bibliographic record
Abstract
Abstract There is a growing interest in physical model testing of the reservoir and large-scale sand control testing for oil sands. These experiments require the synthesization of representative sand-packs. Particle size distributions (PSDs) of these sand-packs ought to be comparable to the PSD of target oil sands. For practical and economic reasons, it is favorable to test samples with a limited number of PSDs, yet representative of a spectrum of oil sands. The aim of this paper is to categorize the PSD of Alberta oil sands to a limited but representative number for use in laboratory research. This paper is based on the analysis of 152 PSD curves for Alberta oil sands. To categorize these PSD's in a meaningful way, an algorithmic approach is presented which uses attributes that are widely used in sand control design (e.g. D10, D50, D70, fines content) and, subsequently screens and sorts the data to produce a finite number of PSD categories which represent the majority of the data. Rules are implemented in the algorithm to limit the number of categories (≤7), and require that each category cover a significant subset of the total data (≥10%). A review of the published PSDs for oil sands across Alberta indicates a significant variation in the PSD curves even within the same reservoir. However, in spite of the fact that PSD data show a large variation, PSD categories can be identified to build representative oil sand samples for design and testing purposes. For the database used in this investigation, four major and two minor PSD classes were identified. These six PSD classes, cover more than 87% of the analyzed PSDs. Introduced classes and existing PSD classifications in the literature share interesting similarities. However, certain differences, such as the lack of very coarse ranges (D50~500 µm) was observed. The method which is introduced for oil sand classification is based on the D-values which are commonly used in screen aperture design. This method provides a useful tool for both screen designers and researchers to categorize and focus their work on a specific set of representative PSDs, rather than a wide distribution of PSDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".