Interdependent effects of injection volume and rate on fault slip behavior: A large-scale numerical study
Bibliographic record
Abstract
Various injection parameters have been shown to pose significant effects on human-induced seismicity due to a variety of activities such as wastewater injection, carbon storage and geothermal energy production. In this study, we used numerical modeling to investigate how different injection parameters, namely injected volume and injection rate, affect the behavior of faults in the context of fluid-induced seismicity. We tested a large model space (4500 simulations) and modeled injection cases with both spatially homogenous and heterogenous pore-pressure perturbations. Simulation results showed that the two parameters can have various impacts on fault behavior, and that in some cases their effects are interconnected. We discovered that aseismic slip plays a significant role in altering the timing of triggered earthquakes and has lasting impacts on future seismic activity. Moreover, we found that increasing the injection rate tends to increase the size of the triggered cluster of earthquakes, while increasing the injection volume increase the overall rate of earthquakes. We find that spatial heterogeneity has qualitatively similar effects as compared to spatially homogenous cases, with a few quantitative differences. Lastly, we also performed a case study of an injection scenario based on realistic values of pore-pressure diffusion and injection operations in Oklahoma, and we found that for an injection duration of one year, the pore pressure on the faults in the region does not go back to zero even after 70 years and can cause earthquakes years after the end of injection, perturbing the seismic cycles for ~200 years. Our work has potential important implications for safe operation of injection processes which can reduce the risk of seismic hazards.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".