Detecting long-period long-duration microseismic events during hydraulic fracturing in the Cline Shale Formation, west Texas: A case study
Bibliographic record
Abstract
A reservoir’s well production rates can be enhanced by hydraulic fracturing. Predicting a formation’s response to this stimulation is of great importance to understanding the total deformation that results from hydraulic fracturing. Long-period long-duration (LPLD) waveforms are a dominant deformation mechanism during hydraulic stimulation (Zoback et al. 2012). If this assertion is correct, then it is important to detect and locate where these unusual events occur in order to better understand this slip mechanism. We evaluated microseismic data recorded by means of downhole and surface array acquisition during the hydraulic fracturing of a horizontal well located in Glasscock County in the Permian Basin to detect the presence of LPLD microseismic events. LPLD waveforms were detected in the downhole acquired data, but the examination of the data recorded on the surface geophones did not reveal the presence of slow slip motion most likely due to the low signal strength. Because of the long duration of these events, no distinct P or S arrivals, and the characteristic of being dominated by low frequencies, LPLDs are likely to go unnoticed in the analysis and interpretation methods applied to conventional microseismic 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 | 0.000 |
| 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) | 0.000 | 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 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".