Petroleum potential assessment using an optimized fuzzy outranking approach: A case study of the Red River petroleum system, Williston Basin
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
This paper presents a new approach, called optimized fuzzy Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), based on combining fuzzy logic, an outranking method and the cuckoo search optimization algorithm to assess petroleum potential in a spatial framework. The approach brings together flexibility and simplicity to solve petroleum exploration problem under uncertainty using experts’ knowledge and the information associated with the discovered oil pools simultaneously. The characteristics of the essential elements of the petroleum system are used as key criteria in the model. To exemplify the approach, a case study was undertaken in the Red River petroleum system of the Canadian portion of the Williston Basin. Eight datasets related to the selected criteria were integrated by the optimized fuzzy PROMETHEE to create a map that makes it possible to identify the areas of highest petroleum potential. All discovered oil pools in the Red River petroleum system were used in the verification process. The results indicated that the proposed approach can deal effectively with incomplete data and imprecise information, and can be efficiently used in petroleum exploration, thereby reducing the cost and risk of exploration.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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