High resolution seismic imaging of a shallow gas reservoir, Alberta, Canada
Bibliographic record
Abstract
Shallow gas reservoirs are often found at surprisingly shallow depths; while they can provide an inexpensive drilling target, they also can be a substantial risk during the initial phases of drilling. As such, it can be important to look for such hazards prior to drilling. Seismic imaging of such shallow geological targets, however, is always a challenging task for the oil & gas explorationist or the engineering geophysicist. Here we present results of high resolution seismic survey conducted in Northern Alberta, Canada to image an ultra shallow geological target. The main objective of this survey was to explore and image (<100 meter) shallow gas bearing sand reservoir. Commercial gas was in production since early to mid 90's in this area. Both high resolution seismic and electrical resistivity tomography was used. We have already discussed some of the unique aspects of the preliminary processing of these data (e.g. Ahmad et al., 2005a,b; Ahmad and Schmitt, 2005b). This contribution focuses on some special processing applied to this unique data set in order to further highlight the near surface gas zone.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".