Processing and quality-control strategies for consistent time-lapse seismic attributes: A case history on an internal blowout using vintage data
Bibliographic record
Abstract
Many vintage reflection seismic surveys exist that have nonrepeated acquisition geometries or final-stacked/migrated sections are obtained using different or nonconsistent processing flows. This may lead to derived time-lapse attributes that are not internally consistent or even conflicting. For instance, we have focused on a subsurface gas blowout that occurred in 1989 in the Norwegian sector of the North Sea. The 2D site survey data were acquired in 1988 and 1990, and the 3D data were acquired in 1991 and 2005. The various sets of legacy data are plagued by poor repeatability among data acquisitions, application of different processing strategies, missing prestack data, and the presence of multiples. All of these factors severely complicate even qualitative interpretation of the gas migration associated with the underground blowout. Careful time-lapse processing may provide useful information even from such challenging legacy data by first computing numerous attributes including instantaneous amplitude differences, time shifts, time-lapse attenuation, and impedance inversions. Then, judicious quality control, invoked by comparing the various attributes, was used to check for internally consistent results.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".