Application of seismic attributes and spectral decomposition for reservoir characterization of a complex fluvial system: Case study of the Carbonera Formation, Llanos foreland basin, Colombia
Bibliographic record
Abstract
ABSTRACT Hydrocarbon exploration in the Llanos foreland basin of eastern Colombia has traditionally focused on structural traps. However, in the past decade, the country’s oil demand has generated an increased interest in exploration for stratigraphic traps. We integrated 700 km2 of 3D seismic data volumes with eight wells in the Central Llanos area to assess the reservoir potential of the fluvial channel deposits of the Late Eocene–Oligocene Carbonera Formation in the Casanare Province. Distinguishing nonproductive, mud-filled channels from productive sand-filled channels is of economic importance for hydrocarbon exploration because both channel types can exhibit a similar seismic character. Our interpretation of the fluvial sandstone and the reservoir identification was based on 3D seismic attributes, including coherence, curvature, and spectral decomposition, and the analysis of fluvial geomorphology. Analysis of stratal slices through coherence, isofrequency amplitude cubes, and curvature cubes revealed (1) a northeast-trending meandering fluvial system with changes in the rivers’ paleoflow direction from southwest to northeast during the period of the Late Eocene to the Early Miocene, (2) development of prospective sandy point bars, scrolls, and sand bar deposits, and (3) channel ridges and bases accentuated by differential compaction dependent on the channel’s filling material. Based on our attribute analysis, we were able to characterize nonprospective mud-filled channels versus prospective sand-filled channels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".