The Duvernay Formation: The application of structure and simultaneous inversion for reservoir characterization and induced seismicity
Bibliographic record
Abstract
Multi-component inversion from 3-D seismic data integrated with structural interpretation can give vital insights to maximize value from an unconventional play. Here we introduce a workflow which is used for characterizing the Duvernay play in western Canada; a low permeability unconventional reservoir, both in a structural and lithologic sense. This includes the determination of a time-depth relationship using synthetic seismograms, generation of seismic derived structural maps, and the determination of the inversion-based parameters of density and P (compressional) and S (shear) wave velocity. The model-based procedure includes poststack (acoustic) inversion, AVO prestack inversion, and joint PP-PS inversion. With these rock properties determined, calculations are made to determine Young’s Modulus, Poisson’s Ratio and brittleness. Faults are mapped based on time slices, isochrons and observable correlatable vertical displacements. Seismic-derived attributes, when combined with structural mapping, can highlight zones most favorable to hydraulic fracturing. The mapping of structural discontinuities can also lead to an understanding of zones of pre-existing weakness, and an understanding of induced seismicity risk. Presentation Date: Monday, October 15, 2018 Start Time: 1:50:00 PM Location: 213A (Anaheim Convention Center) Presentation Type: Oral
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".