Microseismic Monitoring Reveals Natural Fracture Networks
Bibliographic record
Abstract
Microseismic monitoring is used to visualize fracture growth during hydraulic fracture treatments. Naturally occurring fracture networks within the formation of interest, as well as direction of maximum horizontal stress, play significant roles in determining the way that these fractures propagate. Naturally occurring fracture networks may include large-scale faults, parasitic faults associated with the large-scale faults, and smaller-scale fractures. The locations of microseismic events can be used to visualize induced fractures and atypical natural fractures where they exist. Geological knowledge is valuable to accurately interpret microseismic event locations. Trends in microseismic event locations can illustrate the existence and orientation of naturally occurring fracture networks. Two case studies, one from the Montney Formation in NE British Columbia, Canada, the other from the Barnett Shale in Texas, USA, will demonstrate the effects natural fractures can have on hydrocarbon production. Microseismic event locations from the Barnett Shale and the Montney Formation show that natural fracture networks exist. Wells connected to these networks show higher production values due in part to the higher permeability associated with open fractures. Variations in productivity between wells may be related to the presence or absence of natural fractures. When natural fracture networks are revealed by microseismic monitoring, that knowledge can then be used to optimize drilling and completion programs, which in turn reduces costs and maximizes production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".