Mudstone diversity: Origin and implications for source, seal, and reservoir properties in petroleum systems
Bibliographic record
Abstract
Abstract Mudstone is the most abundant sedimentary rock and variously acts as sources, seals, and shale gas reservoirs in petroleum systems. Many important physicochemical properties of mudstones are strongly influenced by the mineralogy and size of deposited grains, and by diagenetic changes (precompaction and postcompaction); these are commonly predictable. The diverse composition of mudstones reflects input and hydrodynamic segregation of detrital materials to basins, primary production within basins, and diagenetic processes (both precipitation and dissolution) in the sediment. High-magnification observations both in modern and ancient sediments demonstrate that mudstones are texturally and mineralogically heterogeneous; this variability is not always readily apparent. Although some mud is indeed deposited by suspension settling out of low-energy buoyant plumes, textural analyses reveal that it is commonly dispersed by a combination of waves, gravity-driven processes, and unidirectional currents driven variously by storms and tides. Such dispersal mechanisms mean that muddy successions are typically organized into packages that can be interpreted using sequence stratigraphy. Early bioturbation homogenizes mud, whereas early chemical diagenesis can result in highly cemented zones developing, especially at stratal surfaces. The nature of deeper burial diagenesis, which involves compaction, mineral dissolution, recrystallization, mineral reorientation and lithification, and petroleum generation, is preconditioned by depositional and early diagenetic characteristics of the mud. Although the petrophysical properties of homogeneous mudstones are reasonably well known, the quantitative implications of heterogeneity for petroleum expulsion, retention, petroleum migration, seal capacity, acoustic anisotropy, and identification of shale gas reservoir sweet spots are essentially unexplored. Future work should seek to redress this position.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".