The Impact of Stochastic Extraction Cost on the Value of an Exhaustible Resource: The Case of the Alberta Oil Sands
Bibliographic record
Abstract
In a much cited paper, Brennan and Schwartz (1985) demonstrated the application of contingent claims analysis to the valuation of a nonrenewable natural resources project when the decision-maker has flexibility to choose from several modes of operations- open, closed and abandoned. The authors assumed fixed extraction costs and that the price of the resource follows Geometric Brownian Motion. The resulting stochastic optimal control problem must be solved numerically, such as with a finite difference approach. For natural resource extraction projects, uncertain costs are also important in optimal decisions and have been less studied in the literature. An example is the oils sands industry where natural gas is used as energy to extract the bitumen, and contributes more than 25 percent of the total per barrel cost. In this paper, we extend the Brennan and Schwartz (1985) model to account for stochastic extraction cost as well as stochastic convenience yield and resource price, and we study the impact on the value of an oil field and optimal decisions regarding extraction. We show that introducing stochastic extraction cost has a substantial impact on value and on the cut-off prices at which it is optimal for the field to switch from one operation mode to another. We use a relatively new method for the evaluation of American-type options- the Least Squares Monte Carol method- which can more easily deal with multiple stochastic factors than traditional numerical approaches.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".