Separating the Signals from Clay Bound Water and Heavy Oil in NMR Spectra of Unconsolidated Samples
Bibliographic record
Abstract
Low-field nuclear magnetic resonance (NMR), whether implemented in a logging tool, bench top analyser or on-line sensor, cannot detect the complete response of heavy oil or bitumen. Both heavy oil and bitumen relax quickly so the spectra from these samples at room temperature appear mostly at relaxation times less than 10 ms. The contribution of heavy oil to the NMR spectrum is distinct in samples that are free of clays or contain bulk water and it is consequently possible to calculate oil and water content based on NMR spectra. Solids content is then determined by difference. However, samples that contain clays and/or relatively little water produce spectra that are more difficult to interpret because the relaxation times of clay bound water are in the same range as bitumen. Experimental results from mixtures containing a layer of illite, kaolinite or montmorillonite in sand exposed to mild brine shows clay bound water to have a characteristic response. These NMR
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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 | 0.000 | 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".