Running Out of and Into Oil: Analyzing Global Oil Depletion and Transition Through 2050
Bibliographic record
Abstract
A risk analysis is presented of the peaking of world conventional oil production and the likely transition to unconventional oil resources such as oil sands, heavy oil, and shale oil. Estimates of world oil resources by the U.S. Geological Survey (USGS) and C. J. Campbell provide alternative views of ultimate world oil resources. A global energy scenario created by the International Institute of Applied Systems Analysis and the World Energy Council provides the context for the risk analysis. A model of oil resource depletion and expansion for 12 world regions is combined with a market equilibrium model of conventional and unconventional oil supply and demand. The model does not use Hubbert curves. Key variables such as the quantity of undiscovered oil and rates of technological progress are treated as probability distributions, rather than constants. Analyses based on the USGS resource assessment indicate that conventional oil production outside the Middle East is likely to peak sometime between 2010 and 2030. Even if oil production does not peak before 2020, output of conventional oil is likely to increase at a substantially slower rate after that date. Analysis based on data produced by Campbell indicates that the peak of non-Middle East production will occur before 2010. Once world conventional oil production peaks, oil sands and heavy oil from Canada, Venezuela, and Russia and, later, shale oil from the United States must expand rapidly if total world consumption of petroleum fuels is to continue to increase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".