The role of lithological layering and pore pressure on fluid‐induced microseismicity
Bibliographic record
Abstract
Abstract The success of hydraulic fracturing treatments is often judged by the shape and size of the resulting microseismic cloud. However, it is challenging to predict the anticipated microseismic cloud prior to treatment. We use geomechanical modeling to predict the distribution of the microseismicity prior to the hydraulic fracture treatment. We analyze the likelihood of tensile and shear failure due to 1‐D variations in local stresses and rock strengths, induced by layering and pore pressure, for two field cases. The deviation in the local stresses from the regional stress field is induced by vertical variations in stiffness. This promotes failure of the stronger layers instead of the weaker ones since the stronger layers can become essentially load bearing. The simulations and field studies show that (1) microseismic events tend to locate preferentially where layers reach tensile failure due to fluid injection, and the number of events tends to decrease in layers that do not reach tensile failure; (2) shear initiation can occur in different layers from those failing in tension, thereby creating additional fluid migration paths; and (3) reactivation of preexisting fractures may occur due to fluid migration, even if their orientations are unfavorable. Numerical modeling is a significant aid in understanding the interplay of regional and local stresses and associated in situ failure due to variations in rock strength, pore pressure, and stiffnesses. In a wider perspective, it gives fundamental insights into the understanding of earthquakes and fault localization, the mechanisms of fracture development, and the role of fractures on fluid circulation and on the in situ stress field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".