Incorporating 3C seismic data quantitatively for enhanced geologic detail in an oil sands reservoir
Bibliographic record
Abstract
Abstract A workflow incorporating converted-wave (PS) data in an integrated quantitative interpretation (QI) was illustrated with a case study in the Canadian oil sands in which multiple data types were available including high-quality 3D multicomponent data, dipole sonic logs, and a multicomponent walk-away vertical seismic profile (VSP). In an area with unconventional rock property behavior and complex fluid distributions, the dipole sonic logs provided the data necessary for robust deterministic rock-physics templates. The VSP was essential in depth-registering the P-wave surface seismic with the PS-wave data and in determining the appropriate phase rotation of each data set. The 3D multicomponent seismic was used to derive a variety of separate and joint attributes incorporating amplitude variation with offset, prestack and poststack inversion and multiattribute processes. Finally, all elements of the workflow were combined in an interactive classification procedure for optimum representation of geology in the seismic volume. Results of the QI workflow with and without the PS data were compared with each other, and ultimately, to blind wells to assess the potential benefits of including PS data. The comparison showed that better prediction of fluid properties, without compromising P-wave data resolution, was possible when PS data were included.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".