Seismic attribute analysis in hydrothermal dolomite, devonian slave point formation, Northeast British Columbia, Canada
Bibliographic record
Abstract
Recent advances in visualization technology and seismic attribute analysis are beginning to revolutionize the landscape of 3‐D seismic interpretation. This presentation focuses on the interpretive use of post‐stack seismic attributes for seismic reservoir characterization. Multiple seismic attributes facilitate structural interpretation and recognition of seismic stratigraphy, but as importantly, they may offer clues to lithology typing and estimation of fluid content from seismic data. Potential benefits include reduction of stratigraphic and structural drilling risks, seismic reservoir characterization in exploration settings, and value increase of new and vintage 3D seismic data. Immediate improvements in drilling risk reduction can be obtained by using multiple seismic attributes. This enhancement occurs because each seismic attribute computation resembles a non‐linear filter that decomposes reflection data into its constituents, and, as a consequence, use of multiple seismic attributes restores much of the discriminating information retained in the originally recorded wavefield (Barnes, 2001; Taner, 2001). Thus, each seismic attribute, for instance, amplitude, inadvertently contains only a subset of the total information recorded, since a single seismic attribute represents only one numerical property of a propagating seismic wavefield. In this presentation, we advocate the use of geometric attributes in conjunction with relative acoustic impedance and frequency‐derived seismic attributes. In the past, use of geometric attributes was mostly limited to edge detection, where edges in the seismic data commonly represent faults or stratigraphic terminations (seismic facies changes). In this Devonian Slave Point Formation case study, we use post‐stack seismic attributes to • Determine the azimuths of conjugate fracture trends in the subsurface • Identify “leaky” vs. sealing fault segments and possible migration/charge/escape pathways. • Identify “sweet spots” in the subsurface Blind testing helped confirm validity of interpretational approach in identifying porous dolostone facies and has resulted in ranking of future prospect locations. 3‐D seismic examples are from northeast British Columbia, Canada (Bubbles Survey) and were provided by Olympic Seismic. Results from this case study should find application in seismic exploration for fractured and hydrothermally altered carbonates worldwide.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| 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 | 0.000 |
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".