Geostatistical Assignment of Reservoir Properties on Unstructured Grids
Bibliographic record
Abstract
Abstract Reservoir simulation is often performed on irregular nonCartesian grids. A common methodology for building the input reservoir models is to perform geostatistical reservoir models on a fine grid and then to average them to the coarser unstructured grid. This method is computationally expensive; a more efficient approach is to modify the geostatistical algorithms to directly populate the unstructured grid. The required modifications are described in this paper. First, direct simulation must be used in place of the more common Gaussian simulation. This is required because reservoir properties do not average linearly after Gaussian transformation; averaging is required because each grid block potentially has a different volume. Second, volume averaged variogram or covariance values are required between two arbitrary blocks v1(u) and v2(u’). These must be calculated quickly and efficiently. Third, to maintain a reasonable speed of geostatistical simulation on unstructured grids a customized search and a non-stationary covariance lookup table of the average covariance between blocks is required. Finally, directional permeability requires a special transformation to account for the nature of averaging. We present the implementation details and some results using tartan and radial grids.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".