Mudstone (“shale”) depositional and diagenetic processes: Implications for seismic analyses of source-rock reservoirs
Bibliographic record
Abstract
Abstract Source-rock reservoirs are fine-grained petroleum source rocks (“shales” or “mudstones”) having geomechanical properties that allow those rocks to produce hydrocarbons at economic rates after stimulation by hydraulic fracturing. Many of the assumptions commonly adopted by geophysicists to characterize shales cannot be applied to source-rock reservoirs. For example, the mineralogies of many source-rock reservoirs are not dominated by clay minerals and so mathematical and/or conceptual models developed for clay-dominated mudstones are not appropriate and cannot be applied to them. Instead, mudstones of shale plays are generally dominated by biogenic calcite and/or quartz. We use terminology of sedimentary geology to show that anisotropy is scale-dependent in source-rock reservoirs, and we discuss the depositional and diagenetic processes that control these and other geophysical properties of interest. The mudstones of source-rock reservoirs may or may not be anisotropic at the lamination scale (i.e., millimeters), the scale commonly used to measure anisotropic parameters via core plugs, but they are nearly always anisotropic at the bedset (centimeters to several meters) and member (tens of meters) scales. Because of the anisotropic nature of mudstones, elastic properties are not scalars at the length/thickness scales that can be defined using seismic methods. Properties of interest are likely to be different parallel to bedding compared to perpendicular to bedding. Because of the subseismic scale of much of this variability, thin-bed effects are likely to influence the AVO behavior of source-rock reservoirs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".