Geomechanical modeling of induced seismicity resulting from hydraulic fracturing
Bibliographic record
Abstract
Abstract The number of instances of induced seismicity associated with hydraulic fracturing has increased over the last few years, resulting in the development of industry protocols to mitigate seismic hazard. The main focus of these protocols is “traffic-light” systems based on seismic monitoring, in which operations are modified if a specified “yellow-light” magnitude level is reached or ultimately are stopped at a “red-light” magnitude. A variety of operational changes is possible to mitigate the seismic hazard at the different traffic-light levels, including slowing injection rate or volume, skipping stages, or ultimately stopping or flowing back the well. Empirical evidence of induced-seismicity magnitudes, including microseismic-imaging projects in which no induced seismicity occurred, are inconclusive about the impact of changing volume or rate. Although the largest observed magnitudes occur at large injection volumes, significant variability in magnitudes is found for both injection rate and volume. Alternatively, a geomechanical simulation can examine pore-pressure diffusion and mechanical stresses and strains associated with hydraulic-fracture treatments and can be used to model fault activation and corresponding estimates of seismic magnitudes. These geomechanical models complement monitoring-based traffic-light systems and can be used to test various operational changes to identify a scenario that reduces seismic hazard.
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 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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".