Understanding Induced Fracture Complexity in Different Geological Settings Using DFIT Net Fracture Pressure
Bibliographic record
Abstract
Abstract A review of several hundred Diagnostic Fracture Injection Tests (DFITs) from vertical and horizontal wellbores in a variety of lithologies (tight sandstone, siltstone and shale) within a spectrum of geological settings (passive margin, foreland, and active strike-slip/thrust basins) was conducted to determine potential controls on stimulation complexity determined from the DFIT Net Fracture Pressure (NFP). Not surprisingly large differences in NFP complexity exist within this diverse data set and the variability is best explained by grouping the data according to the tectonic setting of the basin. Tectonic setting is interpreted as the first order control on NFP complexity since increasingly complex tectonic and burial histories elevate stresses and create tectonic fractures that promote increasingly complicated interactions between induced hydraulic fractures and intrinsic rock fractures. As a result, rocks in the Gulf Coast passive margin basin (Haynesville, Bossier) which have relatively simple burial and tectonic histories exhibit the lowest NFP complexities whereas rocks in strike-slip/thrust basins with high present day tectonic stress and abundant tectonic fabric have the largest complexity. Rocks in foreland basins (Montney, Horn River, Cretaceous Deep Basin sandstones) have NFP complexity that is generally variable between passive margin and strike-slip/thrust basins. Within any particular tectonic setting, NFP complexity is controlled by a complicated interplay between the natural fracture intensity, net horizontal stress (NHS) and wellbore geometry. Increasingly stiff and brittle rocks are commonly increasingly naturally fractured and this favours greater NFP complexity, and DFITs from vertical wellbores generally exhibit lower NFP complexity as fracture initiation and growth is simpler from vertical wells than from horizontal wells. Relations between NFP complexity and NHS (closure – pore pressure) are complicated by the degree to which tectonics has diminished and overprinted the pore pressure control on closure stress. In the Gulf Coast passive margin setting (eg.Haynesville shale) pore pressure is the dominant control on closure stress and NFP complexity is increased where pore pressures are lower, possibly due to frac geometry changes associated changing stress profiles. In more tectonically complex settings (foreland and strike-slip/thrust basins) pore pressure exerts less influence on closure while tectonic stresses increasingly influence regional NFP complexity.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".