Complete Decision-Tree Analysis Using Simulation Methods: Illustrated With an Example of Bitumen Production in Alberta Using Steam Injection
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In September 2003, the SPE held a workshop on the future of asset valuation in corporate asset design and selection process. One suggestion that came out of the workshop was that methods should be developed that will allow the use of decision tree analysis not only in the exploration and appraisal phases of the asset life cycle, but throughout the cycle, and in such a way that the decisions to be analysed are made not only in response to the resolution of geological uncertainty but also in response to the resolution of price and other commercial uncertainty. This will require a significant expansion of the computational technology available to deal in decision tree analysis with complex policy spaces in the face of complex structures of underlying uncertainties. Work has been gong for several years in financial markets to deal similar issues using different subtle combinations of simulation and optimisation methods. Two of these combinations the Longstaff Schwartz method and the stochastic programming method have been applied recently to valuations in the mining industry in situations that are analogous to those faced by the upstream petroleum industry. In this paper, we show how the Longstaff-Schwartz method, as adapted for use in a mining context, may be applied to the analysis of decision-making and value in the development of a bitumen deposit in Alberta using steam injection, where the underlying uncertainties are price movements in the bitumen produced and the natural gas used to produce the steam, and technological and geological uncertainties in the production and cost profiles. The analysis is complicated by the fiscal regime that brings to bear at each time the past price history through a particular form of ring-fenced resource rent royalty.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it