Using high-resolution microresistivity image logs to reconstruct paleoenvironments and stratal architectures: An example from the McMurray Formation, Leismer area, northeastern Alberta, Canada
Bibliographic record
Abstract
ABSTRACT Point-bar deposits, although readily recognizable in most successions, are notoriously challenging to map and correlate accurately, particularly in the subsurface. Sedimentologic interpretations from microresistivity image logs, by contrast, offer an unparalleled method of accurately reconstructing point-bar architectural elements and mapping their distribution. Using an example from the Lower Cretaceous McMurray Formation, we demonstrate that the bedding dip profiles of stacked downstream migration, lateral accretion, and channel abandonment elements record the semicontinuous point-bar accretion of two erosionally juxtaposed large-scale channels. Microresistivity image-log analysis clearly demonstrates that bedding and architectural elements of these heterolithic point bars display discrete and predictable changes in dip angle and direction that are independent of autogenic variations in lithology. We advocate, therefore, that this approach be used to map the facies of point bars and predict their reservoir trends. The laterally accreted mud-sand couplets display a cyclic pattern that is consistent with a nonrandom recurring process. Based on the distribution of bioturbation in the mud beds and variations in sand-bed thicknesses, we argue that this recurring process was probably a monthly occurrence. By measuring the lateral-accretion bed thicknesses for what appears to be a 1-yr interval, a sedimentation rate of 86 cm/yr is estimated for these lateral-accretion beds. This deposition rate provides an upper limit required to deposit these point bars and improves the framing of the depositional model. We estimate that the two point bars in this study were each deposited over a period of a few hundred years.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 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".