7 Sequence Sets and Composite Sequences
Bibliographic record
Abstract
ABSTRACT This chapter presents definitions, recognition criteria, and examples of sequence sets and composite sequences within a sequence-stratigraphic framework. This stratigraphic scale provides useful insights into shale-gas and tight-liquid plays with mudstone reservoirs that can be profitably grouped into four families based on stratal stacking at the sequence-set scale. Depositional sequences stack in progradational, aggradational, retrogradational, or degradational patterns to form sequence sets—exactly analogous to the stacking patterns of parasequences within depositional sequences discussed in Bohacs et al. (2022a, Chapter 6 this Memoir). Successions of sequence sets accumulate between lower-order sequence boundaries to form lower-order composite sequences containing lowstand, transgressive, and highstand sequence sets. Each of the component, “higher order,” sequences has all the stratal attributes of a depositional sequence, including constituent parasequences and systems tracts, that play a dominant role in controlling the distribution of reservoir, source, and sealing mudstones. Nonetheless, the relative development (thickness and character) of systems tracts in higher-order sequences is strongly influenced by the lower-order stacking pattern of those sequences. Thus, lowstand systems tracts tend to be better developed in depositional sequences within lowstand sequence sets, transgressive systems tracts are better developed in transgressive sequence sets, and so forth in each respective portion of the composite sequence. These repeated stacking patterns of strata and surfaces enable prediction of lithofacies character and distribution, both away from sample control and below the resolution of typical seismic-reflection data. For example, the most widespread, fine-grained, and biogenically dominated strata in the proximate shelfal areas of a composite sequence tend to occur near the top of the transgressive sequence set. Large-scale sequence-set stratigraphic analysis indicates that mudstone reservoirs do not occur randomly but have a repeated and predictable distribution within one of four families—and that such analysis is essential for understanding the localized variations in reservoir potential and distribution. The shared attributes within each family provide objective criteria for selecting appropriate analogs among mudstone reservoir plays and highlight the utility of conducting a basin-to-play–scale stratigraphic analysis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".