Triggering Mechanisms and Mitigation Strategies of CO2 Injection-Induced Seismicity in the Canada Weyburn Field
Bibliographic record
Abstract
Abstract Induced seismicity poses a key technical and regulatory challenge for the long-term deployment of carbon captureand storage (CCS) projects. This study investigates the geomechanical response of the Weyburn reservoir to sustained CO2 injection using a fully coupled thermal-hydrological-geomechanical-chemical (THMC) simulation framework implemented in CMG-GEM. A 3D geological model was constructed based on integrated core analysis, wireline logs, and seismic interpretation to account for structural heterogeneity and fault networks. The simulation results indicate that pore pressure buildup is the primary driver of fault instability, especially in proximity to critically stressed faults. Although thermal contraction and mineral dissolution contribute to changes in stress and stiffness, their effects remain secondary under typical injection scenarios. Field-recorded microseismic events from 2003 to 2010 align spatially with modeled zones of elevated pore pressure, particularly along a northeast-trending fault system. These findings underscore the importance of incorporating fault geometry and in-situ stress conditions into storage site evaluation and operational planning. The work establishes a reliable modeling approach to support seismic risk management in geological CO2 storage projects and informs the design of safe injection strategies.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".