Application of focal-time analysis for improved induced seismicity depth control: A case study from the Montney Formation, British Columbia, Canada
Bibliographic record
Abstract
ABSTRACT Characterization of induced seismicity and associated microseismicity is an important challenge for enhanced oil recovery and development of tight hydrocarbon reservoirs. In particular, accurately correlating hypocenters of induced events to stratigraphic layers plays an important role in understanding the mechanisms of fault activation. Existing methods for estimating focal depth, however, are prone to a high degree of uncertainty. A comprehensive analysis of inferred focal depths is applied to induced events that occurred during completions of horizontal wells targeting the Montney Formation in British Columbia, Canada. Our workflow includes a probabilistic, nonlinear global-search algorithm (NonLinLoc), a hierarchical clustering algorithm for relative relocation (GrowClust), and depth refinement using the recently developed focal-time method. The focal-time method leverages stratigraphic correlations between P-P and P-S reflections to eliminate the need for an explicit velocity model developed specifically for hypocenter depth estimation. We find that this approach is robust in the presence of noisy picks and location errors from epicenters obtained using a global-search algorithm, but it is limited to areas where multicomponent 3D seismic data are available. We have developed a novel method to determine statics corrections to ensure that the passive seismic observations and 3D seismic data share a common datum in areas of moderate to high topography. Our results highlight the importance of transverse faults, which appear to provide permeable pathways for activation of other faults at distances of up to 2 km from hydraulic fracturing operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".