Empirical and Numerical Investigation of the Effects of Hydraulic Fracturing Injection Rate on the Magnitude Distribution of Induced Seismicity Events
Bibliographic record
Abstract
Abstract Hydraulic fracturing operations to enable production from unconventional oil and gas reservoirs have been subject to public, industry, and regulator concerns regarding induced seismicity. The injection of fluids into deep formations to generate hydraulic fractures serves to create localized increases in pore pressures and reductions in the effective normal stresses acting on critically stressed faults, resulting in fault slip and induced seismicity. Amongst the different factors influencing induced seismicity, operational factors such as injection volume and rate are potentially important, and can be controlled (in contrast to geological factors, which cannot). In this paper, an empirical study is presented examining correlations between injection rate and volume and induced seismicity events and magnitudes for data compiled for the Montney play in northeastern British Columbia. The results of the empirical analysis show that injection rate has a slightly higher correlation to induced seismicity than injection volume, and that larger events (>M3) correlate with higher injection rates (>6-8 m3/min). Three-dimensional numerical modelling was also performed to further investigate the magnitude distribution of induced seismic events as a function of different injection rates. For the modelled geological scenario, the results indicate that lower injection rates resulted in a more distributed pore pressure perturbation interacting with an adjacent critically stressed fault, resulting in multiple slip areas producing several small magnitude events. In contrast, higher injection rates resulted in a more concentrated pore pressure perturbation interacting with the fault causing a larger area to slip, producing a singular large magnitude event.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".