The influence of stratigraphic architecture on seismic response: Reflectivity modeling of outcropping deepwater channel units
Bibliographic record
Abstract
The size, shape, stacking patterns, and internal architecture of deepwater deposits control reservoir fluid flow connectivity. Predicting deepwater stratigraphic architecture as a function of position along a deepwater slope from seismic-reflection data is critical for successful hydrocarbon exploration and development projects. Stratigraphic architecture from confined and weakly confined segments of a deepwater sediment-routing system is analyzed in outcrop from the Tres Pasos Formation (Upper Cretaceous), southern Chile. Outcrop observations are the basis of two geocellular models: confined channel deposits at Laguna Figueroa and weakly confined channel and scour deposits at Arroyo Picana. Key stratigraphic surfaces and facies relationships observed in outcrop are forward seismic modeled at high to low resolution to bridge the gap in subseismic scale interpretation of deepwater reservoirs and demonstrate challenges associated with identification of varied reservoir architecture. The outcrop-constrained geometry of architectural elements, their stacking arrangement, and the varied internal distribution of facies each impart a strong influence on seismic reflectivity. Key outcomes from the analysis include (1) stratigraphic architecture transitions down-paleoslope from vertically aligned low-aspect-ratio channel elements to a more weakly confined depocenter characterized by a breadth of laterally offset low- and high-aspect-ratio channel and scour elements. Seismic reflections, down to 30 Hz frequencies, record aspects of these stratigraphic changes. (2) Key seismic reflections are often comprised of multiple outcrop-constrained stratigraphic surfaces. Tuning effects result in composite seismic surfaces that are vertically offset from the known position of sedimentary units; this hinders accurate interpretation of stratigraphic surfaces from seismic-reflection data. This is particularly problematic in the weakly confined system in which shifted stratigraphic surfaces, which bound deposits characterized by numerous similar architectural elements, can alter the interpretability of sandstone connectivity within and across zones. Furthermore, misinterpretation of surfaces is problematic when they are flow barriers draped with debris flows, slumps/slides, or thin-bedded turbidites. (3) Tuning effects also impart significant control on volume-based interpretations from seismic data. In particular, calculations of gross rock volume from seismic reflection data that do not consider the tuning or architectural element stacking pattern can overestimate actual volumes by 10%–50%, with implications for reservoir prediction and hydrocarbon volume estimation.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".