Microseismic Monitoring of a Duvernay Hydraulic-Fracturing Stimulation, Alberta Canada: Processing and Interpretation assisted by Finite-Difference Synthetic Seismograms
Bibliographic record
Abstract
The increase in the development of unconventional oil and gas reservoirs in the past years has triggered anomalously high seismic activity in several sedimentary basins around the world, particularly in North American basins located in central and eastern United States and in western Canada. The Duvernay shale play, located in central Alberta, Canada, is an example of the seismicity-triggering effect by hydraulic fracturing stimulations required to produce hydrocarbons from low-permeability formations. To better understand the seismic mechanisms associated with hydraulic fracturing in this area, a local seismic monitoring array comprised by short-period and broadband sensors, was temporally installed to monitor a multi-stage hydraulic-fracturing (MSHF) stimulation on four horizontal wells drilled in the Duvernay Formation near the town of Fox Creek, Alberta, where multiple earthquakes associated with HF have been reported in recent years. This thesis presents a robust workflow for modelling and processing passive seismic data acquired with this local monitoring array, in order to automatically detect and characterize the microseismicity associated with the monitored MSHF stimulation. This characterization includes the epicentre location, depth, magnitude, fault size, and radiated energy of all the detected microseismic events, and the focal mechanisms of the seismic events with the largest magnitudes (up to ML 3.77 in this study). The obtained microseismicity is then integrated with other datasets from the monitored reservoir (well logs and production from the monitored and nearby wells, and 3D seismic) to identify reactivated faults that triggered the largest-magnitude seismic events detected during this monitoring program, and to characterize the unconventional reservoir to forecast the hydrocarbon production after the stimulation. A magnitude scale based on the duration of coda waves was also calibrated for the study area in central Alberta, which can be implemented similar seismic monitoring programs for magnitude estimation as it does not require the installation of broadband sensors. Finally, the generated ground motions of the seismic events with the largest magnitudes detected in this dataset, were determined using the local and regional seismic monitoring arrays to assess the uncertainty of a set of Ground Motion Prediction Equations (GMPE) recently developed for the Fox Creek Area at close and distant hypocentral distances. These GMPEs are fundamental to quantify the seismic hazard of induced earthquakes to nearby communities and infrastructure.
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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 | 0.000 |
| 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".