Surface Microseismic Mapping Reveals Details of the Marcellus Shale
Bibliographic record
Abstract
Abstract The mapping of microseismic events induced by hydraulic fracturing plays an important role in well completion and design. This is especially true in a newly developing area of gas producing shales. In this case study, we will show how the microseismic monitoring of a hydraulic fracture treatment in the Marcellus Shale identified a pre-existing natural fault which intersected the wellbore. The data from nearby wells indicated several possibilities of structural evolution affecting the producing formation. These range from regional reverse or strike-slip faulting to small displacement local reverse faulting. The hydraulic fracture stimulation was monitored using a 10 line, radial surface array composed of 1000 vertical component geophone stations. The treatment consisted of seven perforated stages stimulated with slickwater and proppant. Microseismic activity mapped during the early stages of the treatment is consistent with the regional stress direction and indicates that stages 1-4 activated natural fractures oriented along the maximum horizontal stress direction. During stages 5 and 6, the hydraulic fracture encountered a pre-existing natural fault. A source mechanism was determined for events occurring along the fault, identifying oblique failure with strike-slip and reverse faulting along the steeply dipping fault with SSE strike. This indicates that the regional strike-slip fault, with a strike similar to the break we observed at other offset wells, is most likely responsible for the geological evolution of this formation.
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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".