Heavy Oil Quantification Using Nuclear Magnetic Resonance and Elemental Spectroscopy Technologies in McMurray Formation, <i>Mannville Group, Lower Cretaceous</i>
Bibliographic record
Abstract
Abstract The Athabasca oil sand is one of the three major bitumen deposits in northern Alberta. It contains about 80% of the Alberta total bitumen deposits. The McMurray Formation, Mannville Group, Lower Cretaceous, which contains the Athabasca oil sands bitumen deposit, covers about 46800 square kilometers at an average thickness of 60 meters. The total recoverable reserve is estimated to about 170 billion barrels, which is the second largest oil reserve after Saudi Arabia. Variation in the formation water resistivity (Rw) and difficulties to accurately estimate the shale volume (Vsh) over the heavy oil zones present a real challenge in water saturation calculation, thus in total reserves estimation. A combination of Nuclear Magnetic Resonance and Elemental Spectroscopy Technologies, with conventional logs, permitted us to quantify the different components of the heavy oil in the reservoir over the McMurray formation. The technique is to use the clay volume, directly measured with the nuclear spectroscopy tool, to discern the "visible to NMR" heavy oil component from CBW in the 2D NMR analysis. The "invisible to NMR" heavy oil component is estimated using the lithology corrected density porosity and the NMR total porosity. The total heavy oil saturation of one of the five studied wells is compared to core oil saturation and to nuclear spectroscopy oil saturation, diredctly derived from the measured carbon. Excellent matching between the three results is seen. This approach presents advantages over the classic petrophysical methods, mainly in zones with variable connate water salinity and variable clay minerals. Its application can be extended to any bitumen or heavy oil deposits and will help in reducing coring programs.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".