Natural fracture patterns at Swift Reservoir anticline, NW Montana: the influence of structural position and lithology from multiple observation scales
Bibliographic record
Abstract
Abstract. Subsurface datasets typically lack the resolution or coverage to adequately sample fracture networks in 3D, and fracture properties are typically extrapolated from available data (e.g. seismic data or wellbore image logs). Here we assess the applicability of extrapolating fracture properties (orientation, length, and intensity) across observation scales in deformed, mechanically layered carbonate rocks. Data derived from high-resolution field images, medium-resolution digital outcrop data, and relatively low-resolution satellite imagery at Swift Reservoir anticline, NW Montana are leveraged to (i) assess interacting structural and stratigraphic controls on fracture development, and (ii) compare estimated fracture properties derived from multiple observation scales. We show that hinge-parallel and hinge-perpendicular fractures (i) make up the majority of fractures at the site; (ii) are consistently oriented with respect to the fold hinge, despite along-strike variability in the fold hinge orientation; and (iii) exhibit systematic increases in intensity towards the anticline hinge. These fractures are interpreted as having formed during folding. Other fractures recorded at the site exhibit inconsistent orientations, show no systematic trends in fracture intensity, and are interpreted as being unrelated to fold formation. Fracture orientation data exhibit the greatest agreement across observation scales at hinge and forelimb positions, where hinge-parallel and hinge-perpendicular fracture sets are well developed, and little agreement on the anticline backlimb, where fracture orientations are less predictable and more dispersed. This indicates that the scaling of fracture properties at Swift Reservoir anticline is spatially variable and partly dependent on structural position. Our results suggest that accurate prediction and extrapolation of natural fracture properties in contractional settings requires the assessment of structural position, lithologic variability, and spatially variable fracture scaling relationships, as well as consideration of the deformation history before and after folding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".