Unsupervised seismic facies classification applied to a presalt carbonate reservoir, Santos Basin, offshore Brazil
Bibliographic record
Abstract
ABSTRACT Mapping of seismic and lithological facies is a very complex process, especially in regions with low seismic resolution caused by extensive salt layers, even when only an exploratory view of the distribution of the reservoir facies is required. The aim of this study was to apply multi-attribute analysis using an unsupervised classification algorithm to map the carbonate facies of an exploratory presalt area located in the Outer high region of the Santos Basin. The interval of interest is the Barra Velha Formation, deposited during the Aptian, which represents an intercalation of travertines, stromatolites, grainstones and spherulitic packstones, mudstones, and authigenic shales, which were deposited under hypersaline lacustrine conditions during the sag phase. A set of seismic attributes, calculated from a poststack seismic amplitude volume, was used to characterize geological and structural features of the study area. We applied k-means clustering in an approach for unsupervised seismic facies classification. Our results show that at least three seismic facies can be differentiated, representing associations of buildup lithologies, aggradational or progradational carbonate platforms, and debris facies. We quantitatively evaluated the seismic facies against petrophysical properties (porosity and permeability) from available well logs. Seismic patterns associated with the lithologies helped identify new exploration targets.
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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) | 0.006 | 0.007 |
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; both teacher heads agree on what is shown here.
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".