Microseismic signatures of hydraulic fracture growth in sediment formations: Observations and modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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 it