Mechanical stratigraphy in Mesozoic rocks of the San Juan Basin: Integration of stratigraphic and structural terms and concepts
Bibliographic record
Abstract
We characterize relationships between stratigraphy and natural fractures in outcrops of Mesozoic strata that rim the San Juan Basin in New Mexico and Colorado. These outcrops expose fluvial and shallow-marine siliciclastic deposits and calcareous mudstones deposited in a distal marine setting. We focus primarily on a regionally extensive fracture set formed during the Eocene to minimize localized tectonic effects on fracture development. Where possible, we supplement our observations with wireline log- or laboratory-derived measurements of rock properties. Our goals are twofold: 1) to illustrate how direct integration of data and concepts from stratigraphy and structural geology can lead to better fracture characterization, and 2) to develop thought processes that will stimulate new exploration and development strategies. Genetic beds form one scale of stratification in the outcrops we describe. For example, sandstone beds can be arranged into coarsening and thickening upward successions that are the depositional record of shoreline progradation. In fluvial settings, cm- to dm-scale sandstone beds can also be part of m-scale single-storey channel complexes that, themselves, can be arranged into amalgamated channel complexes 10s of m thick. In these and other settings, it is important to distinguish between beds and features that can be defined via wireline logs because it is the former (cm- to dm-scale) that are usually the primary control the distribution of natural fractures. The extension fractures we describe are typically bed-bound, with bedding being defined by lithology contrasts and the associated changes in elastic properties. Fracture spacing distributions are typically lognormal with average spacing being less than bed thickness. Although mechanical bedding and depositional bedding are commonly the same, diagenesis can cut across bed boundaries and complicate this relationship, especially where lithologic contrasts are small. Deposits from similar depositional environments which undergo different diagenetic histories can have substantially different mechanical properties and therefore deform differently in response to similar imposed stresses.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".