Heavy Oils and Oil Sands: Global Distribution and Resource Assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Global recoverable resources of heavy oil and oil sands have been assessed by CNPC using a geology‐based assessment method combined with the traditional volumetric method, spatial interpolation method, parametric‐probability method etc. The most favourable areas for exploration have been selected in accordance with a comprehensive scoring system. The results show: (1) For geological resources, CNPC estimate 991.18 billion tonnes of heavy oil and 501.26 billion tonnes of oil sands globally, of which technically recoverable resources of heavy oil and oil sands comprise 126.74 billion tonnes and 64.13 billion tonnes respectively. More than 80% of the resources occur within Tertiary and Cretaceous reservoirs distributed across 69 heavy‐oil basins and 32 oil‐sands basins. 99% of recoverable resources of heavy oil and oil sands occur within foreland basins, passive continental‐margin basins and cratonic basins. (2) Since residual hydrocarbon resources remain following large‐scale hydrocarbon migration and destruction, heavy oil and oil sands are characterized most commonly by late hydrocarbon accumulation, the same basin types and source‐reservoir conditions as for conventional hydrocarbon resources, shallow burial depth and stratabound reservoirs. (3) Three accumulation models are recognised, depending on basin type: degradation along slope; destruction by uplift; and migration along faults. (4) In addition to mature exploration regions such as Canada and Venezuela, the Volga‐Ural Basin and the Pre‐Caspian Basin are less well‐explored and have good potential for oil‐sand discoveries, and it is predicted that the Middle East will be an important region for heavy‐oil development.
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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 it