Reducing Geologic Uncertainty in Seismic Interpretation: Case Study of Lower Mannville Channels in Western Canadian Sedimentary Basin
Bibliographic record
Abstract
Summary When dealing with seismic and well data interpreters often face certain challenges characteristic of both data types. Well data is very detailed vertically and gives rich detail in specific locations, but the rest of the field remains unknown at that level of detail. Seismic data is almost nearly the opposite; it provides very good resolution laterally, but is much less detailed vertically and typically doesn’t provide a direct measurement of physical properties of interest. Combining both data types, geologic models capable of filling in the gaps between seismic and well data sets have become exceedingly valuable. In this investigation we studied a number of uncertainty reducing workflows associated with both forward and inverse modeling techniques. How can we make predictions as to what attributes will uniquely discriminate between reservoir and nonreservoir rocks and fluids with confidence? Forward modeling of geophysical data uses well-defined geological models to calculate specific seismic field responses. Using available log data combined with geologically reasonable model constraints, geomodelers may construct a number of modeled seismic responses that can be used to validate or annul various working geologic models. In contrast, geophysical inverse modeling techniques attempt to construct a physical property model based off a geophysical response.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".