Integrated analysis and interpretation of microseismic monitoring of hydraulic fracturing in the Marcellus Shale
Bibliographic record
Abstract
In 2012 and 2013, hydraulic fracturing was performed at two Marcellus Shale well pads, under the supervision of the Energy Corporation of America. Six lateral wells were hydraulically fractured in Greene County in southwestern Pennsylvania and one lateral well was fractured in Clearfield County in north-central Pennsylvania. During hydraulic fracturing operations, microseismic monitoring by strings of downhole geophones detected a combination of >16,000 microseismic events at the two sites. High quality traditional and geomechanical well logs were acquired at Clearfield County, as well as tomographic velocity profiles before and after stimulation. In partnership with the US Department of Energy’s National Energy Technology Laboratory, I completed detailed analysis of these geophysical datasets to maximize the understanding of the engineering and geological conditions in the reservoir, the connection between hydraulic input and microseismic expression, and the geomechanical factors that control microseismic properties. Additionally, one broad-band surface seismometer was deployed at Greene County and left to passively monitor site acoustics for the duration of hydraulic fracturing. Data from this instrument shows the presence of slow-slip or long period/long duration (LPLD) seismicity. In years prior to our investigation, lab-scale fracturing studies and broadband seismic monitoring of hydraulic fracturing had been completed by other researchers in unconventional shale and tight sand in Texas and Canada. This is the first study of LPLD seismicity in the Marcellus Shale and reveals aseismic deformation during hydraulic fracturing that could account for a large portion of “lost” hydraulic energy input. Key accomplishments of the studies contained in this dissertation include interpreting microseismic data in terms of hydraulic pumping data and vice versa, verifying the presence of LPLD seismicity during fracturing, establishing important geomechanical controls on the character of induced microseismicity, and extensive data integration toward locating a previously unmapped fault that appears to have exhibited significant control over well stimulation efforts at Clearfield.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".