Improvement in Facies Discrimination Using Multiple Seismic Attributes for Permeability Modeling of the Athabasca Oil Sands, Canada.
Bibliographic record
Abstract
The objective of this reservoir modeling study was to predict the permeability distribution which has impact on the performance of SAGD (Steam Assisted Gravity Drainage), the in-situ bitumen recovery technique. Lithologic facies of a fluvial-estuary channel system observed in the study area are classified into three groups having different characteristics of permeability. Discrimination of the three lithologic facies is a key step in permeability modeling, because different facies use different formulas to estimate facies permeabilities. Seismic data contribute to the lithologic facies prediction by improving facies probability to be used in geostatistical facies modeling. In particular, this study employs a probabilistic neural network utilizing multiple seismic attributes for further improvement of the facies probability. Improvement in facies prediction due to using multiple seismic attributes was demonstrated by comparison with using only a single seismic attribute. Adding P-wave velocity to the group of multiple seismic attributes is a key to enhanced facies discrimination. This paper also discusses a possible cause of the different P-wave velocity of different facies, where sand matrix porosity is uniquely evaluated using a cross-plot of log-derived porosity and photographically predicted mudstone volumes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".