Oil Sand Characterization for Standalone Screen Design and Large-Scale Laboratory Testing for Thermal Operations
Bibliographic record
Abstract
Abstract Oil sand characterization tests are essential for the selection and evaluation of sand control devices. Current approaches for screen selection and evaluation usually rely on Particle Size Distribution (PSD) and neglect the effect of important parameters such as porosity, grain shape and frictional properties. One aim of this study is to characterize oil sand's mechanical, geometrical and size characteristics that should be considered in the completion design. Another objective is to determine if natural mixture of oil sand could be reasonably replicated with commercial sands for large-scale sand control evaluation tests. In this paper we present the results of a comprehensive image analysis and laser sieve analysis on oil sand samples from the McMurray Formation to quantify geometrical grain characteristics (sphericity, aspect ratio, convexity and angularity) of the sand grains and establish the PSD of the samples. Direct shear tests were performed to assess the frictional characteristics of different oil sands around the liner under variable stress conditions during the SAGD well lifecycle. Image analysis, PSD, and direct shear tests showed that natural mixture of oil sand could be successfully simulated with commercial sands in terms of size and shape of grains and mechanical properties. This conclusion is significant to those performing large-scale sand control evaluation tests that usually require large quantities of sands that are not readily available and require significant preparation. This paper provides the first comprehensive investigation of the granular, and geomechanical characteristics of oil sand from the McMurray Formation. The paper discusses the missing parameters in the design of sand control device, and evaluates test methods that measure those parameters. The proposed testing program could be used as a benchmark for oil sand characterization in relation to the design and evaluation of sand control device.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".