Coulomb Stress Failures in Fracture Networks Resulting from Hydraulic Fracturing: A Tutorial
Bibliographic record
Abstract
Summary In hydrocarbon-producing sedimentary basins, the crust behaves as a poroelastic medium. The empirical Coulomb failure criterion is applicable to this medium. The stress state of the poroelastic medium, which entails both the shear and normal stress of the rock, is influenced by the pore fluid diffusion. Coulomb stress changes due to pore pressure changes in a poroelastic medium lead to microseismic events. This is also true of any hydraulic fracturing where the fluid injection changes the pore pressure and hence, changes in the mean stress of the medium. Despite the fact that the poroelastic dynamics is highly complex even with homogeneous properties, understanding the physical process of pore-fluid induced microseismicity is critical to planning and carrying out successful hydraulic fracturing experiments. In this tutorial, we introduce the concept of Coulomb stress failure changes as applied to the study of regional static stress changes in tectonically active earthquake areas causing spatially predictable aftershocks. We consider the reasons to think beyond the double-couple mechanism as a source characteristic for microseismicity due to hydraulic fracturing. Finally, we propose a complex network model of microseismic events with every event caused by pore fluid relaxation subsequent to a pore-pressure change so that a time-dependent, composite Coulomb stress failure map of the medium under modified stress is realizable.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".