Decision Making in the Presence of Geological Uncertainty With the Mean-Variance Criterion and Stochastic Dominance Rules
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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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".