Decision Making in the Presence of Geological Uncertainty With the Mean-Variance Criterion and Stochastic Dominance Rules
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary At the heart of petroleum reservoir management (PRM) resides the challenge of selecting the best project from a group of feasible candidates in the presence of geological uncertainty. The challenge is particularly relevant in low-oil-price investment environments where many upstream projects are economically marginal and must be optimized. Companies are now more cautious. Investors are aware that they should consider not only the rewards of the projects but also their risks. For these reasons, the selection of projects to be implemented in the field should consider the geological risk and the capacity of the companies to tolerate it. In this paper, we introduce a decision-making model for active geological-risk management. The model is consistent with the utility theory framework and combines the mean-variance criterion (MVC) and stochastic dominance rules (SDRs) to guide the selection process. Two examples in the context of steam-assisted gravity drainage (SAGD) are presented.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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