An Improved Regional Segmentation for Probability Perturbation Method
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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