An Improved Regional Segmentation for Probability Perturbation Method
Bibliographic record
Abstract
Summary Conventional history matching methods do not consider seismic and geologic continuity data. Caers (2002) introduced a novel history matching method named Probability Perturbation Method (PPM) by extending the multiple-point geostatistics framework to production data; The method’s key point is to perturb the underlying probabilities used to generate properties and not the properties directly. In single region PPM, one perturbation parameter is used for the entire reservoir. However, in multi-parameter perturbation, different amounts of perturbation are applied to different parts of reservoir In our method, a weight factor is assigned to each point (well location) in a way that the volume of each generated region is proportional to the rate of well located inside the region. In other words, volume divided by rate is equal for all regions. Therefore, the question is how to find the weight factors. A set of equations is formed and the solution is found by an iterative method. In each time step, the weight factors and consequently regions could be updated based on well rates. The Voronoi diagram has already been used for defining regions, however the novelty of this work is that defined Voronoi regions are proportional to rate and update dynamically without flow simulation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".