Permeability Modeling for the SAGD Process Using Minimodels
Bibliographic record
Abstract
Abstract The predicted flow performance of Steam Assisted Gravity Drainage (SAGD) well pairs is sensitive to the spatial distribution of permeability. A number of permeability measurements are taken from small scale core plug data. The data may be taken preferentially from certain geologic locations and there may be inconsistencies in the data. The measurement scale is significantly less than that required for input to flow simulation. Mini-models of porosity and permeability are constructed and flow simulated in order to establish representative relationships/correlations at the grid block scale used in SAGD flow simulation. The mini-models are constructed on a by-facies basis honoring the spatial variability within each category. The uncorrected mini-model flow results lead to a too-narrow range of permeability. Geostatistical scaling laws are applied to correct the permeability values. This paper presents a permeability modeling procedure with application to the Surmont Lease in Northern Alberta, Canada. The mini-model construction, flow simulation of the mini-models, and derivation of representative porosity-permeability statistics are described and documented in this context. A comparison of SAGD flow simulation results (recovered bitumen and steam-oil-ratio) with different permeability modeling procedures is presented to support the relative importance of modeling permeability. The legitimacy of any particular permeability model can only be compared to or validated with comparison to actual flow performance at some time in the future, which is a difficult task. Even with actual flow data, this is a difficult task. This will occur at the Surmont project, at some point in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".