Characterising Relay Structures in Carbonate Rift Systems and the Implications for Structural and Depositional Reservoir Uncertainty
Bibliographic record
Abstract
Summary Segmented fault systems and relay structures are recurrent features in rifts and faulted continental margins that play a critical role in determining flow behaviour associated with fault seal variations and depositional drainage patterns along fault zones ( Fossen & Rotevatn, 2016 ). However, the prevalence and extent to which interpreted faults are segmented beyond conventional imaging and modelling resolutions is typically oversimplified through the interpretation of faults as singular, planar structures often too coarse to represent the constituent segments of the fault zone. We present a workflow that utilises the interrogation and analysis of high-resolution, interpretation-scale displacement profiles that can provide critical insight into the potential extent of fault segmentation, drawing on fundamental principles of fault growth and evolution to identify missed and/or obscured structural architectures that are key to more reliable structural modelling and reservoir characterisation workflows. Examples are presented from carbonate reservoirs of the Santos Basin in the Brazilian southeastern coast, where complexities in large faults that have been impacted by pre-salt seismic imaging challenges are addressed through the reinterpretation of additional fault segments and the application of semi-automated, holistic relay zone and fault segmentation characterization diagnostics.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".