The use of microresistivity image logs for facies interpretations: An example in point-bar deposits of the McMurray Formation, Alberta, Canada
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Bibliographic record
Abstract
ABSTRACT Well logs such as spontaneous potential and gamma ray historically have been the only tools available for facies evaluation of noncored wells in the McMurray Formation. The addition of microresistivity image logs has greatly improved facies identifications and interpretations, particularly when integrated with core data sets. In the case of McMurray channel complexes, core descriptions provide detail about bedding contacts, sedimentary texture, stratification, bioturbation intensity, and trace fossil diversity. Image logs provide texture, fabric, bedding contact style, dip directions and angles, and bedding architecture information, yielding paleoflow and lateral accretion directions. This study characterizes facies by integrating interpretations from 414 image logs with core-based descriptions from 138 of these wells. The reservoir targets, and most prolific depositional facies in this study, are associated with channel systems and their associated point-bar deposits. Facies identifications are based on several image log criteria. Mud clast breccias display variable dip angles and dip directions. Cross-stratified sands comprise vertical successions of stacked, internally consistent bedsets with high dip angles (>15°) that indicate paleoflow direction. Lateral accretion beds show consistent dip directions with a progressive change from shallow-to-steep-to-shallow dip angles (e.g., <4 to 15° to <4°) from the base to the top of the succession, as well as beds that dip toward the thalweg of the paleochannel. Flat-lying (<4°) mud records vertical accretion associated with point-bar tops or channel abandonment. Although this facies classification is specific to the McMurray Formation in the study area, the principles provided here are applicable to other subsurface studies and demonstrate the enhanced reliability of integrated core–image log data sets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it