Future of the Canadian Oil Sector: Insights from a Forecasting-Planning Approach
Bibliographic record
Abstract
iiG{2013{19Les Cahiers du GERADAbstract: It is increasingly important to provide the relevant data for strategic decisions related to oilproduction and the marketing of oil products. We propose the use of a forecasting model to de ne a productionpro le for the Canadian oil sector to 2050. Our approach considers both economic variables (prices) andphysical variables (production and infrastructure) by establishing a link between well count, oil price, andoil production. Our methodology is based on practices developed in the oil industry. Indeed, the well countis used as a key component of planning and decision-making in matters such as capital and operationalexpenditures. We combine our approach with the Hubbert logistic function to take into account the impactof the age of the producing wells. We calibrate our forecasting model using a Canadian database of historicalproduction data. The records come from the Eastern Canada o shore and Canadian oil sands projects thatare of growing importance in the national oil production. We test our model under a particular scenariofor oil prices, including an extrapolation of the historical price trends. Our results show the evolution of oilproduction and indicate when peak production is achieved for each of the oil sources considered.Key Words: Forecasting, Hubbert, Well Count, Oil production, Oil prices, Oil reserves, Oil sands, Onshore,O shore, Infrastructure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".