Bibliographic record
Abstract
Summary Hydraulic fracturing of shale reservoirs enhances productivity of reservoirs by propping open fractures in the reservoir. In order to map the extent of the successfully stimulated zones, microseismic monitoring is increasingly used; typical outputs of such monitoring efforts are the geometry of the microseismic event distribution. To relate these event distributions to production decline curves, geomechanical modelling of the injection using these event distributions as a constraint is frequently performed. However, a basic assumption of such efforts is that the stress regime under which the events are occurring is invariant. By using multiple-well recordings of microseismic events, the mechanisms of the microseismicity may be determined. These mechanisms are proportional to the strain rate (deformation) that is imparted to the medium at the point of rupture, and as such constrain the stress regime through the treatment. Observations indicate that the stress/strain conditions in the reservoir can be highly variable, implying that microseismicity needs to be coupled to geomechanical models at a more basic level, in that the dynamic stress regime controls both the occurrence of these events and the propagation of fluid and proppant in the reservoir.
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.
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 | 0.000 |
| 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) | 0.002 | 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".