Geologic heterogeneity representation using high‐order spatial cumulants for subsurface flow and transport simulations
Bibliographic record
Abstract
The effects of geological heterogeneity representation on the hydraulic properties of two‐dimensional flow and transport simulations are studied using various stochastic simulation algorithms. An alternative multiple‐point method (HOSIM) on the basis of high‐order spatial cumulants and Legendre polynomials is used and compared to the multiple‐point FilterSIM method, and the sequential Gaussian simulation (SGS) method. Conditional realizations of a fluvial reservoir system are generated by HOSIM, FilterSIM, and SGS methods. Then, the simulated hydraulic permeability fields (K) are used in a numerical groundwater flow and solute transport models. Numerical results showed that the HOSIM method created greater connectivity in the reservoir/aquifer (channel) network than FilterSIM and SGS realizations. The numerical simulations show that in a reservoir/aquifer system with a strongly connected network of high‐K materials, the Gaussian and FilterSIM approaches are not as effective as HOSIM in reproducing this behavior. The simulations showed a good agreement between HOSIM realizations and the exhaustive reference image.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".