Traffic light system regulation of induced seismicity under multi-well fluid injection
Bibliographic record
Abstract
The occurrence time and magnitude of injection-induced seismicity are influenced by engineering factors, such as wellhead pressure, injection location, injection volume, and injection rate. Understanding the relationship between injection operations and seismic magnitude is of great significance for optimizing industrial production and reducing earthquake disasters. Numerical simulation of hydro-mechanical coupling is a crucial method for studying injection-induced seismicity. However, few studies have explored the risk management measures for injection-induced seismicity from the perspective of engineering. How seismic magnitudes can be reduced through reasonable adjustments to injection operations in engineering remains unclear. Therefore, in this study, a 3D hydro-mechanical coupling model involving multiple faults and injection wells was established based on the geological background and well location of Fox Creek, Canada. Different injection schemes under multi-well and multi-fault conditions were studied, and a traffic light system was used to simulate and control the magnitudes under a multi-well injection scheme. Specifically, we simulated injection scenarios involving up to three wells and analyzed the response of five faults. We compared the maximum moment magnitude of different scenarios by controlling the same injection volume. The results revealed the effect and advantage of the multi-well scheme in reducing seismic magnitude. To reduce the risk of induced seismicity, utilizing far-fault operational wells to compensate for the effects of near-fault operational wells proves to be an efficient and cost-effective method, with potential for wide practical applications. • The effect of multi-well injection on fault mechanical properties is investigated. • The effect is revealed by 3D fluid mechanics coupling numerical simulation. • The magnitude of multi-well injection is examined using traffic light system. • The multi-well injection scheme in reducing earthquake magnitude is revealed. • The use of far-fault working wells is revealed as a cost-effective method.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".