Investigation Into the Use of Commercial Sands and Fines to Replicate Oil Sands for Large-Scale Sand Control Testing
Bibliographic record
Abstract
Abstract This paper presents the characterization of oil sands from the McMurray Formation. The main objective of this paper is to investigate the possibility of replicating the oil sands by the mixtures of commercial sands and fines for large-scale testing. There is a growing interest in large-scale evaluation testing for sand control devices that require considerable amounts of representative oil sands materials. However, natural representative oil sands samples are usually not available or are limited in quantity. Therefore, replicating the oil sands is essential for such tests. Twenty-three oil sands samples were collected from two wells in the McMurray Formation and cleaned using the Soxhlet extraction technique. The cleaned samples were examined using the image analysis technique and Scanning Electron Microscope (SEM) imaging to study their Particle Size Distribution (PSD), shape factors, mineralogy, and texture. Similar analysis was performed on eleven series of commercial sands to compare their shape, mineralogy, and texture with those of oil sands. Particle Size Distribution of 10 commercial fines was also analyzed with a particle sizer to cover the required fine/clay part of the duplicated samples. Direct shear and 1D consolidation were performed to compare the mechanical properties of the oil sands samples and the duplicated mixtures of commercial sands and fines. The shape factors of the oil sand and the selected commercial sand samples are in close agreement. In addition to the common average/cumulative shape factor measurements, this paper also presents the variation of shape factors within each sample for different grain sizes. The results show the same sand shape characteristics among all oil sand samples as well as the tested commercial sands. Further, XRD results indicate a similar mineralogy for the commercial sands and the oil sands samples. The SEM images show random changes in the surface texture of both oil sands and commercial sands with no observable trends. We were able to use commercial sands and fines mixture with similar grain shape properties to duplicate the PSD of the oil sand samples. Direct shear and 1D consolidation testing of the oil sands and samples made of commercial sands and fines show similar consolidation and frictional properties for both the duplicated mixture and cleaned oil sands for the same compaction level (porosities). This paper provides a procedure for duplicating the oil sands with commercial sands and fines. It also provides detailed information on the mineralogy, texture, and the variation of the shape characteristics for oil sands from the McMurray Formation.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".