An Integrated Approach to Permeability Modeling Using Micro-Models
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Spatial distribution of permeability is an important factor in the prediction of performance of Steam Assisted Gravity Drainage (SAGD) well pairs. Presence of short-scale variability in sand/shale sequences, preferential sampling of core data, and uncertainty in upscaling parameters are complications that make the inference of a reliable porosity – permeability relationship impossible. A simple yet effective way of overcoming these complications is micro-modeling. The central idea in micro-modeling is to use an additional source of information, namely digitized core images, to quantify the uncertainty in power-law averaging parameters and construct the porosity-permeability bivariate relationship by Monte Carlo Simulations (MCS). The work-flow in micro-modeling is comprised of a few steps from digitizing the selected core images to building 3D geo-blocks of binary sand/shale mixture, populating them with porosity/permeability values, upscaling the populated binary mixture by flow simulations, determining the uncertainty in power-law parameters and implementing MCS. The porosity-permeability relationships are constructed on a by-facies basis. Results of this research suggest that effective properties of clean sand are changing with the volume fraction of shale; and it has ultimately resulted in the development of an extended version of power-law formalism.
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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 | 0.000 |
| 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