Incorporating 3C seismic data quantitatively for enhanced geologic detail in an oil sands reservoir
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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