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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".