Monitoring increases in fracture connectivity during hydraulic stimulations from temporal variations in shear wave splitting polarization
Bibliographic record
Abstract
Hydraulic overpressure can induce fractures and increase permeability in a range of geological settings, including volcanological, glacial and petroleum reservoirs. Here we consider an example of induced hydraulic fracture stimulation in a tight-gas sandstone. Successful exploitation of tight-gas reservoirs requires fracture networks, either naturally occurring, or generated through hydraulic stimulation. The study of seismic anisotropy provides a means to infer properties of fracture networks, such as the dominant orientation of fracture sets and fracture compliances. Shear wave splitting from microseismic data acquired during hydraulic fracture stimulation allows us to not only estimate anisotropy and fracture properties, but also to monitor their evolution through time. Here, we analyse shear wave splitting using microseismic events recorded during a multistage hydraulic fracture stimulation in a tight-gas sandstone reservoir. A substantial rotation in the dominant fast polarization direction (ψ) is observed between the events of stage 1 and those from later stages. Although large changes in ψ have often been linked to stress-induced changes in crack orientation, here we argue that it can better be explained by a smaller fracture rotation coupled with an increase in the ratio of normal to tangential compliance (<it>Z</it><inf><it>N</it></inf>/<it>Z</it><inf><it>T</it></inf>) from 0.3 to 0.6. <it>Z</it><inf><it>N</it></inf>/<it>Z</it><inf><it>T</it></inf> is sensitive to elements of the internal architecture of the fracture, as well as fracture connectivity and permeability. Thus, monitoring <it>Z</it><inf><it>N</it></inf>/<it>Z</it><inf><it>T</it></inf> with shear wave splitting can potentially allow us to remotely detect changes in permeability caused by hydraulic stimulation in a range of geological settings.
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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 | 0.000 | 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 | 0.001 |
| 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 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".