Conceptual analysis of induced seismicity risks associated with Hydraulic Fracturing (HF) and Wastewater Disposal (WWD) in Cadomin Formation A Formation-Adaptive Risk Assessment Framework
Bibliographic record
Abstract
This research investigates the risks of induced seismicity associated with hydraulic fracturing (HF) and wastewater disposal (WWD) in the Cadomin Formation, a subsurface unit shared between Alberta and British Columbia, Canada. Recent seismic events linked to injection activities highlight the limitations of current regulatory frameworks, which rely on generalized thresholds and reactive protocols rather than formation-specific risk assessments. To address this gap, the study develops a conceptual and exploratory framework that integrates Bow-Tie Analysis and Layers of Protection Analysis (LOPA) to systematically identify threats, barriers, and consequences, with barrier performance classified within the Hierarchy of Controls. The framework is demonstrated through Cadomin Formation, while comparative insights from the Duvernay Formation illustrate how lithological and stress-regime differences shape barrier reliability and initiating frequencies. Screening-level LOPA tables were applied as semi-quantitative tools to show how geological and operational conditions influence the reliability of preventive and mitigative safeguards. These outputs are not definitive probability calculations but structured indicators of relative risk reduction. In the absence of direct operational data, this research adopts a conceptual but rigorous approach grounded in geological and engineering principles. The bow-tie analyses directly informed the positioning of risks within both the risk matrix and LOPA screening, ensuring consistency across tools. The results emphasize that while reactive regulatory protocols such as the Traffic Light System provide short-term guidance, they do not fully address long-term liabilities or cumulative risks. Proactive monitoring, advanced modeling, and improved regulatory clarity are needed to strengthen resilience. Ultimately, this study contributes a formation-specific, auditable framework that advances induced seismicity risk management from reactive thresholds toward predictive, adaptive strategies. It offers methodological insights rather than prescriptive standards, providing a foundation for future research, regulator–operator collaboration, and the development of standardized, formation-specific protocols across HF, WWD, and emerging subsurface injection technologies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".