Hydro-shearing and traffic light protocols in mitigating seismic risks: A fully-coupled poroelastic boundary integral modeling approach
Bibliographic record
Abstract
In low-permeability geothermal reservoirs, hydro-shearing of pre-existing natural fractures plays a crucial role in improving connectivity between injection and production wells, thereby enhancing heat extraction efficiency. This process increases fracture conductivity through dilation caused by injection-induced slip; however, it also carries the risk of inducing seismic events, posing significant challenges for geothermal operations. This study employs a coupled hydro-mechanical numerical model based on the boundary element method to simulate hydro-shearing under two distinct fluid injection scenarios: (1) monotonic injection and (2) cyclic injection regulated by a traffic light system (TLS). The model assesses the effectiveness of these injection regimes in enhancing fracture conductivity while mitigating seismic hazards . Results indicate that monotonic injection frequently triggers a cascade of seismic events, disrupting pressure and stress distributions on nearby faults and resulting in complex seismic and aseismic interactions. In contrast, TLS-regulated cyclic injection, when carefully managed, promotes stable slip behavior and improves fracture conductivity. This approach proves particularly effective over extended durations during the simultaneous stimulation of two parallel faults. However, in multi-stage stimulation scenarios—where natural fractures are stimulated sequentially—TLS-based cyclic injection, while more efficient at enhancing conductivity, may increase seismicity risk with prolonged application, thereby limiting its safe operational window.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".