Role of fracture opening in triggering microseismicity observed during hydraulic fracturing
Bibliographic record
Abstract
ABSTRACT Hydraulic fracturing in low-permeability hydrocarbon reservoirs creates/reactivates a fracture network leading to microseismic events. We have developed a simplified model of the evolution of the microseismic cloud based on the opening of a planar fracture cavity and its effect on elastic stresses and pore pressure diffusion during fluid injection in hydraulic fracturing treatments. Using a material balance equation, we compute the crack tip propagation over time assuming that the hydraulic fracture is shaped as a single penny-shaped cavity. Results indicate that in low-permeability formations, the crack tip propagates much faster than the pore pressure diffusion front thereby triggering the microseismic events farthest from the injection domain at any given time during fluid injection. We use the crack tip propagation to explain the triggering front observed in distance versus time plots of published microseismic data examples from hydraulic fracturing treatments of low-permeability hydrocarbon reservoirs. We conclude that attributing the location of the microseismic triggering front purely to pore pressure diffusion from the injection point may lead to incorrect estimates of the hydraulic diffusivity by multiple orders of magnitude for low-permeability formations. Moreover, the opening of the fracture cavity creates stress shadow zones perpendicular to the principal fracture walls in which microseismic triggering due to the elastic stress perturbations is suppressed. Microseismic triggering in this stress shadow region may be attributed mainly to pore pressure diffusion. We use the width, instead of the longest size, of the microseismic cloud to obtain an enhanced diffusivity measure, which may be useful for subsequent production simulations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".