Analysis of the internal architectural elements of tidal-influenced meandering fluvial deposits using well logging and seismic data: The study of the Athabasca Oil Sands, Alberta, Canada
Bibliographic record
Abstract
Abstract A reservoir was dominated by inclined heterolithic stratification (IHS) formed in large point bars of the McMurray Formation. We have used high-resolution seismic data and logging data to identify the internal architectural elements of the reservoir. From the core data, we defined four lithofacies and recognized the architectural element boundary. Then, we used stratum dip data across wells, combined with seismic reflectivity, isopach, and amplitude attributes, to understand the lateral continuity of the boundaries. Later, we established the sedimentary model and found the differences between tidal-influenced meandering fluvial channels and conventional meandering fluvial channels. Research showed that tidal bedding was especially well-developed, and breccia deposition and muddy IHS were also frequent. The development of the lateral accretion packages was more frequent than that in the conventional meandering fluvial channels. The characteristics of the interbedded layers in sandy IHS were very thin, mainly approximately 20 to 40 cm. The dip of the lateral accretion packages was smaller and distributed from 4° to 8°. The studies were expected to have a major impact on the understanding of reservoir formation, distribution, and heterogeneity for improved hydrocarbon recovery purpose in the area.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".