An Integrated Approach to Permeability Modeling Using Micro-Models
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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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".