Identifying fault activation during hydraulic stimulation in the Barnett shale: Source mechanisms, <i>b</i> values, and energy release analyses of microseismicity
Bibliographic record
Abstract
Identification of fault planes that intersect horizontal wellbores is critical to optimizing formation stimulation, preventing waste of valuable time and materials, and avoiding the establishment of fluid flow pathways into non-target formations, such as aquifers. We can detect and locate microseismic events accurately over a broad area using a large near surface seismic monitoring array. In addition, source mechanism inversion techniques can be used to determine the method of failure experienced by the rock formation, expanding our understanding of the dynamics involved in hydraulic fracturing. Events in this analysis are segregated into two populations based upon the distinct source mechanisms present. Spatial and temporal analysis of frequency magnitude distributions (FMD) allows us to characterize trends useful in assessing the hydraulic treatment efficiency. This information can assist in interpretation of faults in a 3D seismic volume to delineate faults in reservoirs, or when used alone, identify faults of subseismic displacement to further optimize future well placement. Also b values and source mechanisms can help to better define stimulated reservoir volumes (SRV) by indicating the effective level of stimulation.
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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 | 0.000 |
| 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".