Using Multivariate Data Classification on Frontier Exploration Basins to Enhance the Information Value of Suboptimal 2D Seismic Surveys for Unconventional Reservoir Characterization
Why this work is in the frame
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Bibliographic record
Abstract
We developed a workflow that allows integrating legacy 2D seismic surveys with modern log and core data, validating their consistency, classifying them into rock classes with consistent properties, propagating material properties across each of these rock classes, and using this information to improve reservoir characterization and the assessment of their hydrocarbon resource potential. As proof of concept, we analyzed two intersecting 2D seismic lines shot in 2001 in a frontier basin in Canada to determine the distribution of reservoir quality. Each of these had been separately prestack inverted, but have modern core and log data (as well as legacy log data) which were integrated with the inverted attributes.Results identify a most prospective class for reservoir quality within the zone of interest, and show that it increases in thickness to the south in the seismic section.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it