Quality assessment of microseismic event locations and traveltime picks using a multiplet analysis
Bibliographic record
Abstract
The proliferation of hydraulic fracturing (frac) stimulation and other enhanced oil recovery techniques in unconventional plays has spurred interest in microseismic monitoring. Changes in stress in the subsurface produced by hydrocarbon production may induce brittle failure events. Many enhanced oil recovery techniques such as hydraulic frac stimulation or cyclic steam stimulation involve injecting large volumes of fluid at high pressure into a reservoir. Microseismic events, hereafter events, may illuminate the reach and effectiveness of enhanced recovery techniques within a reservoir. When array configuration is favorable, advanced analysis of microseismic data may provide additional information. For instance, seismic moment tensor inversion (Eaton and Forouhideh, 2010) may estimate the failure mode of an event. The aforementioned failure modes may include fracture-opening, shear, or fracture-closing events. Knowledge of the failure modes allows a detailed analysis of the effects of changing parameters such as steam injection temperature or well spacing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".