Measurement of the normal/tangential fracture compliance ratio (<i>Z</i><sub><i>N</i></sub>/<i>Z</i><sub><i>T</i></sub>) during hydraulic fracture stimulation using S‐wave splitting data
Bibliographic record
Abstract
ABSTRACT We develop a method to invert S‐wave splitting (SWS) observations, measured on microseismic event data, for the ratio of normal to tangential compliance ( Z N / Z T ) of sets of aligned fractures. We demonstrate this method by inverting for Z N / Z T using SWS measurements made during hydraulic fracture stimulation of the Cotton Valley tight gas reservoir, Texas. When the full SWS data set is inverted, we find that Z N / Z T = 0.74 ± 0.04. Windowing the data by time, we were able to observe variations in Z N / Z T as the fracture stimulation progresses. Most notably, we observe an increase in Z N / Z T contemporaneous with proppant injection. Rock physics models and laboratory observations have shown that Z N / Z T can be sensitive to (1) the stiffness of the fluid filling the fracture, (2) the extent to which this fluid can flow in and out of the fracture during the passage of a seismic wave and (3) the internal architecture of the fracture, including the roughness of the fracture surfaces, the number and size of any asperities and the presence of material filling the fracture. These factors have direct implications for modelling the fluid‐flow properties of fractures. Consequently, the ability to image Z N / Z T using SWS will provide useful information about fractured rocks and allow additional constraints to be placed on reservoir behaviour.
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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 | 0.001 |
| 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) | 0.001 | 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".