The Impact of Dipping Velocity Models on Microseismic Event Locations
Bibliographic record
Abstract
Abstract Once a microseism is detected, its source location can relatively easily be identified if the velocity characteristic of the medium traversed by the recorded waveforms is known. Unfortunately, this is rarely the case. Velocity models are used to estimate, with some degree of confidence, microseismic event locations. This work shows how a simple modification to the velocity model, accounting for a 4.5-degree dip supported by geological data, significantly impacts the event final locations during a borehole-based hydraulic fracturing monitoring job. Overall geometry of the hydraulically-induced fracture system interpreted (e.g. height) is the most affected. For instance, when a preliminary event location is selected without introducing the observed structural component of the beds, these measurements could change by as much as fifty percent. For the reservoir engineer, sometimes unaware of the assumptions made at the microseismic processing level, these differences could imply major changes to the field development plans. These results underscore the importance of integrating all available data and implementing well known quality controls before using microseismic monitoring data for reservoir analysis.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".