Variation in induced seismicity productivity by alteration of injection parameters: a comparative case study at three hydraulic fracturing wells in the Kiskatinaw area, British Columbia, Canada
Bibliographic record
Abstract
The Western Canada Sedimentary Basin (WCSB) has experienced an increase in hydraulic fracturing (HF) operations in the last decade, accompanied by an increase in the number of felt earthquakes, including a Mw 4.6 on 17 August 2015 near Fort St. John and a ML 4.5 (Mw 4.2) on 30 November 2018 near Dawson Creek. While only a small percentage of HF operations induce seismicity, the majority of moderate-sized earthquakes occur in close spatial proximity to HF wells and temporal proximity to individual HF injection stages within the tight shale play of the Montney Formation. Whereas statistical analysis of an enhanced seismicity catalog suggests that the majority of seismicity occurs following HF operations in the relatively older and deeper compartments of the Montney Formation (Lower Montney; LM) and a low number of events are associated with the relatively younger and shallower layers (Upper Montney; UM), the detailed association and triggering mechanism(s) remains unclear. In this study, we investigate induced earthquake source parameter variations resulting from spatial and/or temporal alteration of injection parameters, including injection time, depth, and volume, at three well pads operating between 2018 and 2020 in the Kiskatinaw area. We use dense local station coverage to create an enhanced seismicity catalog with double-difference relative hypocenter relocations to highlight potential fault orientations, confirmed by focal mechanism solutions. We estimate static stress drop values at the individual well pads and their variation over time as well as variation with the choice of empirical Greens function. We also investigate the temporal changes of the VP/VS-ratio in localized areas following HF operations as a proxy for increased fracture density and/or compliance. The case study at three specific sites targeting both the UM and LM layers investigates the relative influence of a number of factors on the spatial and temporal distribution of source properties. Factors include the scale of HF injection parameters, the target formation layer, and site-specific factors, such as localized fluid accumulation. Preliminary results show that injection in the UM generally leads to significantly fewer earthquakes than injection in the LM, and that lateral variations in compartment properties may significantly influence the seismic response. Moreover, we investigate if repetitive injection at the same wellhead may repeatedly (re)activate sets of faults/fractures and lead to increased hydraulic connectivity between the target sedimentary layers and deeper, pre-existing basement faults. An increase in connectivity would imply an increased potential for triggering large mainshocks.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".