Chasing Australia’s unconventional resources with point-source, point- receiver, full azimuth surface seismic
Bibliographic record
Abstract
Seismic methods can be utilized in unconventional resources characterization studies to achieve an improved understanding of the entire reservoir heterogeneity, structure and stress orientation. This assists in an identification of production “sweet spots” and more efficient well placement. To enable this type of study with surface seismic, we need to analyse and invert the data not only against offset but also azimuth. This requirement places greater demands on the seismic than would be the case for a purely structural image.In this paper we will describe the use of a potential “best-practice” solution based on experience in Australia and elsewhere for the design and implementation of the high specification “Winnie 3D” seismic survey. This survey featured broad-band point-sources and point-receivers using a non-linear maximum displacement sweep of 1.5 to 110 Hz. The omni-directional symmetrical dense sampling, in combination with long offsets, resulted in uniform azimuthal coverage and extremely high trace density. This design combined with a broad-band acquisition enables azimuthal analysis, inversion and seismic attributes extraction.We will demonstrate how this 3D design, tailored for unconventional targets, allowed extraction of seismic attributes even in the early stages of data processing, enabling detection of anomalies that could be related to shallow igneous intrusions.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".