Examining Hydraulic Fracture Characteristics Based on Induced Microseismicity: A Barnett Shale Case Study
Bibliographic record
Abstract
Abstract Microseismicity can be triggered by various dynamic processes related to a hydraulic fracturing treatment. These processes alter the in-situ stress field inside and around the stimulated reservoir volume, due to both creation of new fractures and fluid leakoff into the surrounding rock matrix. The analysis of spatiotemporal dynamics of fluid-induced seismicity can reveal important characteristics of the hydraulic fracturing process. With the knowledge of treatment data, it can be used in conjunction with the reservoir geomechanical theories in hydraulic fracture growth to investigate the fracture geometry and fluid-rock interactions. By applying these theories to a real microseismic dataset, two types of triggering front expansion patterns are evident. With the presence of a dominant hydraulic fracture, the radius of the triggering front expands linearly with time. Moreover, the microseismic event cloud forms a planar shape with low opening angles (failed by shear), indicating fracture slippages around the major hydraulic fracture. On the other hand, in the case of a complex fracture network with the absence of any major hyfraulic fracture, the triggering front grows non-linearly with time. This scenario can be treated as equivalent to a diffusion model and the microseismic events exhibit a higher fracture of tensile components (either opening or closing) and an equidimensional event cloud. In this study, two stages were analyzed and the derived fracture widths and fluid-loss coeffcients fall into a realistic range of general observations in the context of these two theories.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".