Can 3D Seismic Assist the Subsurface Characterization of Low Permeability Reservoirs? A Western Canada Case Study
Bibliographic record
Abstract
Abstract The appraisal and development of low permeability reservoirs require detailed knowledge of their quality and spatial variation. Different applications of 3D seismic data are used to map reservoir's property variation for subsurface characterization, including structural analysis, reservoir property mapping and seismic lithofacies prediction. Seismic structural attributes were used to map large structural features that compartmentalize the reservoir and to identify enhanced natural fracture zones that may locally increase reservoir's permeability (‘sweet spots’). The prestack AVO and rock physics inversions are used for geomechanical properties calculations and total porosity and mineral fraction volume estimation. The inversion results were further extended for reservoir stress computation including pore pressure and effective stress estimations calibrated to pore pressure and DFIT measurements. Reservoir lithofacies volumes were generated using machine learning workflows and direct probabilistic inversion outputs, with the results generally matching the reservoir's lithology mapped from the core data. Moving forward, the 3D seismic data can successfully assist the subsurface characterization of low permeability reservoirs and can be used to support well placement and to optimize the drilling and completion operations.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".