Prospectivity insights from automated pre-interpretation processing of open-file 3D seismic data: characterising the Late Triassic Mungaroo Formation of the Carnarvon Basin, North West Shelf of Australia
Bibliographic record
Abstract
Waveform data from pre-interpretation processing is used in nine Late Triassic interpretation case studies from an area extending more than 30,000 km2 across the Exmouth Plateau, Kangaroo Trough and Rankin Trend on the North West Shelf of Australia. Events selected from a database of automatically generated surfaces extracted from six large open-file 3D marine surveys (~16,000 km2) are used to analyse reservoirs, seals, and pore fluid within the Brigadier and Mungaroo formations in this peer-reviewed paper. Today, geoscience teams are challenged with vast data sets such as the archived versions of more than 125 Carnarvon Basin 3D seismic surveys. Pre-interpretation processing delivers a database of numerous seismic events that cannot be effectively managed using traditional interpretation workstations. With, however, a 3D viewer to query, edit and merge the results, geoscience teams are able to review many large surveys and the surfaces in their interpretation workflows. At the 2013 WABS Conference in Perth, WA, two papers offered models for the Late Triassic gas reservoirs. These models represent many years of synthesis and integration of data by teams of geoscientists from two of the major operators on the North West Shelf. Validation and corroboration of the proposed models was gained by using selected pre-interpretation surfaces. Stacking patterns, waveform fitness, amplitude and two-way time surfaces from these spatial databases revealed geological insights about the formations, such as their complexity of structure, extent of reservoirs, and continuity of seals, along with a better understanding about the trapping and charge systems of the fields.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".