Interpretation of geologic facies in seismic volume using key rock elastic properties and high-definition facies templates
Bibliographic record
Abstract
We have developed a deterministic workflow in mapping the small-scale (centimeter level) subseismic geologic facies and reservoir properties from conventional poststack seismic data. The workflow integrated multiscale (micrometer to kilometer level) data to estimate rock properties such as porosity, permeability, and grain size from the core data; effective porosity, resistivity, and fluid saturations using petrophysical analyses from the log data; and rock elastic properties from the log and poststack seismic data. Rock properties, such as incompressibility (lambda), rigidity (mu), and density (rho) are linked to the fine-particle-volume (FPV) ranges of different facies templates. High-definition facies templates were used in building the high-resolution (centimeter level) near-wellbore images. Facies distribution and reservoir properties between the wells were extracted and mapped from the FPV data volume built from the poststack seismic volume. Our study focused on the heavy oil-bearing Cretaceous McMurray Formation in northern Alberta. The internal reservoir architecture, such as the stacked channel bars, inclined heterolithic strata, and shale plugs, is intricate due to reservoir heterogeneity. Drilling success or optimum oil recovery will depend on whether the reservoir model accurately describes this heterogeneity. Thus, it is very important to properly identify the distribution of the permeability barriers and shale plugs in the reservoir zone. Dense vertical well control and dozens of horizontal well pairs over the area of investigation confirm a very good correlation of the geologic facies interpreted between the wells from the seismic volume.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".