From Microseismic to Induced Seismicity: Monitoring the Full Band of Reservoir Seismicity
Bibliographic record
Abstract
Summary Seismic monitoring is an important tool for evaluating hydraulic fracture treatments in many petroleum reservoirs. Microseismic data is used to determine the extent of fracturing due to treatment and evaluate how effectively the reservoir is stimulated. Induced seismicity monitoring has become important recently, as the occurrence of high magnitude (MW > 0) events in several locations has led to the introduction of government-mandated “traffic light” systems to mitigate the impact of induced seismicity on the general public. To better understand the reservoir conditions which lead to the generation of large events, these two different ways of measuring seismic activity can be combined, incorporating the highly accurate event location accuracy from downhole microseismic monitoring with accurate source characterization of high magnitude events from surface induced seismicity monitoring. Such a monitoring system allows the full range of seismicity related to hydraulic fracture treatments to be accurately characterized. Combining the recorded data is a technical challenge, but with attention to detail in applying relevant corrections it is possible to achieve a consistent dataset. Data from a large multi-well zipper frac employing the full-band monitoring configuration is discussed in detail to illustrate the benefits of an integrated processing workflow in terms of increased understanding of the fracture process and conditions which lead to high magnitude events.
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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 | 0.001 |
| 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) | 0.002 | 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".