Rethinking World Oil-Shale Resource Estimates
Bibliographic record
Abstract
Abstract World oil shale resources are massive compared to conventional oil, making oil shale one of the world's largest known fossil fuel resources. The estimated oil shale resource in place for various countries is an evolving and growing number. The total world resource estimated by the USGS is currently 2.8 trillion barrels of shale oil; but may be as high as 8 trillion barrels depending on the quality of oil shale deposits considered. In 2009, the USGS updated the resource base estimate for the U. S. Piceance Creek Basin, part of the Green River Formation, from 1.0 to 1.5 trillion barrels of oil shale. More than 1.8 trillion barrels of oil are believed to be trapped in shale in Federal lands in the western United States in the states of Colorado, Utah and Wyoming. This resource alone is over three times the proven reserves of Saudi Arabia. While the Green River Formation is the largest deposit of oil shale, there are over 30 countries with known oil shale deposits world wide. With the price of oil staying well above $50 per barrel, new production technologies being considered by private companies for oil shale development may be economic. As these technologies are developed, resource estimates are also becoming more accurate. This paper describes some of recent resource estimates that have been further analyzed with measures such as resource yield and thickness also being described. Estimates will include the USGS update of the Green River Basin and results of Canadian exploration in the Albert Mines and in the Pasquia Hills. The paper will also provide an overview of global oil shale operations, including those in Estonia, China, and Brazil. Finally, this paper will provide a comparison of world oil shale resources and potential production profiles providing a world view of oil shale resources.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".