Reserve Growth of the World's Giant Oil Fields
Bibliographic record
Abstract
Abstract Analysis of estimated total recoverable oil volume (field size) of 186 well-known giant oil fields of the world (>0.5 billion bbl of oil, discovered prior to 1981), exclusive of the United States and Canada, demonstrates general increases in field sizes through time. Field sizes were analyzed as a group and within subgroups of the Organization of Petroleum Exporting Countries (OPEC) and non-OPEC countries. From 1981 through 1996, the estimated volume of oil in the 186 fields for which adequate data were available increased from 617 billion to 777 billion bbl of oil (26%). Processes other than new field discoveries added an estimated 160 billion bbl of oil to known reserves in this subset of the world's oil fields. Although methods for estimating field sizes vary among countries, estimated sizes of the giant oil fields of the world increased, probably for many of the same reasons that estimated sizes of oil fields in the United States increased over the same time period. Estimated volumes in OPE C fields increased from a total of 550 billion to 668 billion bbl of oil and volumes in non-OPEC fields increased from 67 billion to 109 billion bbl of oil. In terms of percent change, non-OPEC field sizes increased more than OPE C field sizes (63% versus 22%). The changes in estimated total recoverable oil volumes that occurred within three 5-year increments between 1981 and 1996 were all positive. Between 1981 and 1986, the increase in estimated total recoverable oil volume within the 186 giant oil fields was 11 billion bbl of oil; between 1986 and 1991, the increase was 120 billion bbl of oil; and between 1991 and 1996, the increase was 29 billion bbl of oil. Fields in both OPEC and non-OPEC countries followed trends of substantial reserve growth.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".