Advanced Seismic-stratigraphic Imaging of Depositional Elements in a Lower Cretaceous (Mannville) Heavy Oil Reservoir, West-central Saskatchewan, Canada
Bibliographic record
Abstract
Abstract We integrated core, wire-line logs, a three-dimensional (3-D) seismic volume, and a seismic attribute-derived 3-D lithology volume to define the stratigraphy of the Lower Cretaceous Mannville Group for a small area in Saskatchewan. The lithology volume was generated by integrating the seismic data with wire-line logs through the use of a probabilistic neural network. The stratigraphic interpretation was an iterative process: first, based on wire-line logs and cores; then, based on the integration of well and 3-D seismic data; and finally, by integrating the attribute-derived lithology volume with the other data sets. Integration of the lithology volume into our stratigraphic interpretation, along with the exploitation of seismic-based visualization technologies, helped us to construct a better geologic model than what could have been constructed using only well data or conventional seismic-stratigraphic analysis techniques. Unfortunately, despite the high-frequency content (and good to excellent quality of the data), meter-scale variations of lithology in the primary reservoir interval could not be detected seismically because of the low acoustic-impedance contrasts between the various lithologies in this interval. Various types of noise in the seismic data also degraded the attribute-based property prediction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 0.001 |
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".