Bibliographic record
Abstract
Abstract The objective of this paper is to examine the effect of oil and gas prices on the development of tight and shale reservoirs including oil, gas and condensate. The methodology uses a variable shape distribution (VSD) model for examining past, present and futures prices of oil and gas. The VSD matches historical annual changes in oil and gas prices with coefficients of determination that are generally larger than 0.98. Contrary to several commentators and specialized organizations around the world that were forecasting continuous oil price escalation up to US$ 300/bbl (e.g. in 2008, when the oil price was around US$ 140/bbl), the VSD was used successfully to anticipate a significant drop in prices, leading to the on-record statement that the oil price was a bubble and was going to burst (June 13, 2008). The VSD was also used to build a case at LACPEC in 2014 (Aguilera and Aguilera, 2014) indicating that "with the vast global oil resource base and the significant technological advances being implemented by the industry, oil prices could actually decrease in the future". This is a painful fact being faced today by the oil and gas industry worldwide. In this paper, the VSD has been used with a global energy market (GEM) model to examine possible future outcomes of unconventional resources development in light of forecasted oil and gas demand and anticipated price-changes calculated with the VSD. The methodology provides a novel approach for examining some realistic possibilities regarding future development of unconventional oil, gas and condensate reservoirs.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".