Non-intrusive telemetry applications in the oilsands: from visible light and x-ray video to acoustic imaging and spectroscopy
Bibliographic record
Abstract
While the production, transport and refining of oils from the oilsands of Alberta, and comparable resources elsewhere is performed at industrial scales, numerous technical and technological challenges and opportunities persist due to the ill defined nature of the resource. For example, bitumen and heavy oil comprise multiple bulk phases, self-organizing constituents at the microscale (liquid crystals) and the nano scale. There are no quantitative measures available at the molecular level. Non-intrusive telemetry is providing promising paths toward solutions, be they enabling technologies targeting process design, development or optimization, or more prosaic process control or process monitoring applications. Operation examples include automated large object and poor quality ore during mining, and monitoring the thickness and location of oil water interfacial zones within separation vessels. These applications involve real-time video image processing. X-ray transmission video imaging is used to enumerate organic phases present within a vessel, and to detect individual phase volumes, densities and elemental compositions. This is an enabling technology that provides phase equilibrium and phase composition data for production and refining process development, and fluid property myth debunking. A high-resolution two-dimensional acoustic mapping technique now at the proof of concept stage is expected to provide simultaneous fluid flow and fluid composition data within porous inorganic media. Again this is an enabling technology targeting visualization of diverse oil production process fundamentals at the pore scale. Far infrared spectroscopy coupled with detailed quantum mechanical calculations, may provide characteristic molecular motifs and intermolecular association data required for fluid characterization and process modeling. X-ray scattering (SAXS/WAXS/USAXS) provides characteristic supramolecular structure information that impacts fluid rheology and process fouling. The intent of this contribution is to present some of the challenges and to provide an introduction grounded in current work on non-intrusive telemetry applications - from a mine or reservoir to a refinery!
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".