Surface Microseismic Monitoring of Slick Water and Nitrogen Fracture Stimulations, Arkoma Basin, Oklahoma
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Surface based microseismic monitoring was performed to assess the effectiveness of slick-water and nitrogen fracture stimulations in a horizontal well with a 3500' lateral drilled in the Arkoma Basin of Oklahoma. Water production from this shale as well as the located events from the microseismic monitoring suggested the fracs were not contained in the target formation and contacted foreign water. The observed distribution of microseismic events suggested that planar fractures were created with varying complexity. The azimuths of the produced trends suggested that a strong influence from the pre-existing natural fractures directed the induced fractures. A direct comparison of the slick-water treatment to the nitrogen treatment revealed multiple advantages with the latter, such as more in-zone events, more energy per event, and more complexity in resulting fractures. Introduction The area monitored is located in the Arkoma Basin of Oklahoma, an area where the basin tectonics are inactive, but the weather is not. The surface array used to perform the surface monitoring of the slick-water and nitrogen treatments was designed to locate induced microseismic events by beamforming. The array consisted of 1078 stations of 12 geophones laid out in a radial pattern around the treatment well (Figure1). Although the treatments were two months apart, the array geometry was identical with the exception of removal of 11 stations from the array during the nitrogen treatment. The geophones were buried to a depth of one foot to maximize the signal to noise ratio by reducing the interference of the frequent seasonal rainfall. Cultural sources of noise such as traffic and inherent pad noises were taken into account by surface array design and seismic processing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it