A priori error estimation of upscaled coarse grids for water‐flooding process
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Bibliographic record
Abstract
Abstract Advanced reservoir characterization methods can yield geological models at a very fine resolution, containing 10 11 –10 18 cells, while the common reservoir simulators can only handle much lower numbers of cells due to computer hardware limitations. The process of coarsening a fine‐scale model to a simulation model is known as upscaling. Predicting the accuracy of simulation results over an upscaled grid with respect to the fine grid is highly important, as it can yield the optimum upscaling process. In this paper, permeability‐based and velocity‐based a priori error estimation techniques are proposed by introducing image processing‐based comparison methods in the context of upscaling. The performance of the introduced error estimation techniques as well as the contribution of the employed image processing method are investigated thoroughly over highly heterogeneous cases under various coarsening levels, permeability upscaling methods, boundary conditions, and mobility ratios. The results show the superior performance of the proposed image processing‐based techniques compared to the existing methods in terms of predicting the accuracy of the water‐flooding process over the upscaled model with respect to the original fine grid results.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 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