Hyperspectral imaging for the determination of bitumen content in Athabasca oil sands core samples
Bibliographic record
Abstract
ABSTRACT Ore grade is one of the primary variables controlling the economic recovery of bitumen from oil sands reservoirs, hence there is a need for fast and reliable quantification of total bitumen content (TBC). This is typically achieved through laboratory-based Dean-Stark analyses of drill core samples. However, this method is time and labor intensive and destructive to the core sample. Hyperspectral imaging is a remote sensing technique that can be defined as reflectance spectroscopy with a spatial context, where high-resolution digital imagery (∼1 mm/pixel [0.04 in./pixel]) is acquired and reflectance measurements are collected in each pixel of the image. This study compares two hyperspectral models for the determination of TBC from imagery of both fresh and dry core samples. For three out of four suites of fresh core, TBC was predicted within ±1.5 wt. % of the Dean-Stark data with both spectral models achieving correlations of R2>0.97. For a fourth fresh core and the dry core, larger margins of error were found because of some instances of overestimation. Surface roughness because of uneven oil distribution and small-scale fracturing is a potential source of error in some of the spectral TBC results, particularly for the dry core. Producing results within minutes with the additional benefit of being nondestructive to the core sample, hyperspectral imaging shows great potential to become a viable alternative method for bitumen content determination in oil sands.
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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".