Ranking Geostatistical Realizations by Measures of Connectivity
Bibliographic record
Abstract
Abstract Geostatistical reservoir modeling provides multiple equally probable realizations of structure, facies, and petrophysical properties. A large number of realizations should be processed to ensure that production decisions and strategies are not unduly affected by an unusually good or bad simulated realization. Flow simulation, however, often requires significant computational and professional time. Only a few geostatistical realizations can be subjected to detailed flow modeling. An integrated approach is developed for ranking geostatistical realizations. A small number of representative realizations can then be selected for flow processing. The ranking and selecting of realizations must be tailored to the flow process. Techniques that work for conventional oil and gas reservoirs are not necessarily suitable for in-situ and SAGD bitumen recovery methods. This paper describes static connectivity measures tailored to heavy oil recovery processes from the McMurray Formation. Flow simulation is performed on many geostatistical realizations to calibrate the ranking measures to production response. This permits reliable inference in reservoir areas where it is not possible to perform many flow simulations.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".