Horizontal variogram inference in the presence of widely spaced well data
Bibliographic record
Abstract
The variogram is a key parameter for geostatistical modelling. Inferring a stable variogram model from widely spaced well data is a longstanding challenge due to an often unreliable experimental horizontal variogram. The main aim of this paper is to improve the horizontal variogram inference in the presence of limited data by quantifying variogram uncertainty and reducing this uncertainty with secondary data. A new approach of variogram uncertainty is presented by computing the number of independent variogram pairs (degrees of freedom) for each lag. A methodology to improve the horizontal variogram uncertainty is developed considering the horizontal variogram of the seismic data and the vertical well variogram since these variograms are well defined in most cases. Seismic data provide constraints on the horizontal variogram of the well data. The constraints are inferred from the covariance between the well and seismic data. The vertical variogram of the well data can be scaled to scenarios of the horizontal variogram. Improved horizontal variogram realizations honouring the correlation between lags are attained by merging variogram distributions for each lag distance considering the constraints from the horizontal seismic variogram. A realistic case study is 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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".