An approach to reduce exploration risk using spectral decomposition, prestack inversion, and seismic facies classification
Bibliographic record
Abstract
ABSTRACT Petroleum companies have invested heavily in the exploration of frontier offshore basins in Brazil. Uncertainties associated with geologic complexity, lithostratigraphy, fluid content, and seismic resolution in these basins are the most common challenges. Many methods have been used to circumvent these issues, such as assessing seismic attributes, seismic inversion, spectral decomposition, and the integration of these methodologies to accomplish an optimal approach. Because of the complex geology of these areas, it is very difficult to identify potential reservoirs within one specific geologic formation. Hence, applying spectral decomposition to frontier basins of high geologic complexity can help overcome the challenges because in the frequency domain, it is possible to better define variability and lateral discontinuities. The correct choice of the spectral decomposition optimization approach is critical for accurate target zone representation as well as selection of the appropriate transform. Prestack inversion, spectral decomposition, and attribute seismic facies classification were applied to evaluate the extent of a reservoir and analyze whether an appraisal well has been sited at a good location. The results showed that the new appraisal well was planned at a good location but that its facies were not as good as those of the first discovery well, as confirmed after drilling.
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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 | 0.000 | 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) | 0.000 | 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".