Automated Microseismic Processing and Integrated Interpretation of Induced Seismicity during a Multistage Hydraulic-Fracturing Stimulation, Alberta, Canada
Bibliographic record
Abstract
ABSTRACT The development of organic-rich, low-permeability formations for hydrocarbon production requires the use of unconventional techniques such as multiwell pad drilling of horizontal wells and massive multistage hydraulic-fracturing stimulations. However, proliferation of these unconventional development methods has been linked to localized cases of fault reactivation during or shortly after hydraulic fracturing. In the Duvernay formation, located in Alberta, Canada, induced seismicity from hydraulic fracturing has occurred on nearly vertical strike-slip faults that are difficult to detect with conventional seismic exploration methods. In such cases, faults may only be discernible from seismic events with precise and accurate locations, which generally requires dense seismic monitoring arrays deployed near the stimulated wells. In this study, we introduce a new, semiautomated workflow for processing passive seismic data from a dense array and then integrate it with a 3D seismic dataset to characterize seismicity clusters related to hydraulic fracturing and pre-existing faults. The reactivated faults inferred from the distribution of the microseismic events directly overlie a system of incised, middle Devonian channels below the Duvernay formation observed in time slices extracted from the 3D seismic data. The channel system exhibits a set of lateral offsets, interpreted as ancient strike-slip fault displacements, the detection of which is further enhanced by use of a similarity attribute calculated from the 3D seismic data. Taken together, integrated interpretation of induced seismicity and 3D seismic data support a model of a regional left-lateral strike-slip fault system that was active during the middle Devonian and reactivated in a reverse sense (right-lateral strike slip) during hydraulic-fracturing operations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".