Microseismic signatures of hydraulic fracture growth in sediment formations: Observations and modeling
Bibliographic record
Abstract
We analyzed a microseismic data set from hydraulic fracture stimulation of the gas field in west Texas. We used an automated wave‐picking algorithm and obtained a high‐density image of induced microseismic events accompanying the hydraulic fracture growth. The microseismic locations delineated a planar fracture growing predominantly in the horizontal direction; the vertical growth was limited by shale layers. A strongly asymmetric fracture with a twice longer eastern wing containing 80% of the located events was observed. Owing to the planarity of the microseismic cloud, it was possible to reduce the location problem to two dimensions and to use only S waves for event localization. Thus, because of the larger amplitudes of S waves, a fourfold increase in the number of located events was achieved. We find that the length of the hydraulic fracture increased, for different depth intervals, both linear and nonlinear in time. We use hydraulic fracture models to explain the spreading of the microseismic front, whose nonlinear time dependence could indicate either a diffusive fluid flow or a two‐dimensional growth of the hydraulic fracture. By the maximum‐likelihood fitting of the observed fracture growth and by inverting for its parameters, we find that the fracture was 7–10 mm wide and that nearly the whole injected volume was used for creating the new fracture, that is a negligible diffusive infiltration of the injected fluid into the reservoir rock occurred.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".