Image analysis of heavy oil liberation from host rocks/sands
Bibliographic record
Abstract
An essential requirement for efficient heavy oil production is liberation of heavy oil from host rocks, which is determined by the wettability of the rocks, and the interfacial tension and viscosity of heavy oil. Recent progress on the design of an online visualization flow cell allows capture of dynamic heavy oil liberation processes from the surfaces of sand grains in real time under a water flooding environment. However, the accurate assessment of heavy oil liberation remains a challenge, due to uncertainties in defining oil‐free areas of heavy oil contaminated rock surfaces. In this study, three new image‐processing algorithms of modified empirical, Gridding, and Edge‐covering methods were applied to image transformation for heavy oil liberation analysis. These methods were found to be more accurate and robust in determining the threshold value distinguishing liberated from unliberated sand surfaces. The use of wavelet transform theory in the Gridding and Edge‐covering methods led to faster calculations with a typical error of less than 2 % in the quantitative analysis on the threshold value determination and the degree of heavy oil liberation. Among these three methods, the Gridding method with a sound theoretical foundation was shown to be the most reliable. The results showed that the threshold value determined was highly dependent on the types of ores and the image capture settings such as lighting conditions, exposure time, and microscope magnification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".