Relationship Between the Hydraulic Fracture and Observed Microseismicity in the Bossier Sands, Texas
Bibliographic record
Abstract
Abstract In the past years, an increased effort was directed to improving our understanding of hydrofracturing by microseismic monitoring and analysis. We are developing a numerical method that would predict microseismicity occurring during hydrofracturing and the influence of fracturing on the permeability of the reservoir. The core of our technique is a representative pre-fractured volume of the reservoir that deforms locally and allows for the coalescence of deformation as the stress reequilibrates. Such an approach allows not only for implementation of the geological and seismic information on the scale of the representative volume but also to follow the deformation at the scale of the complete hydrofracture. We apply this technique to the hydrofracturing of the Bossier sandstone. The main goal is to predict observed microseismicity and permeability changes of the reservoir based on the modeled strains. The results show that the distribution of microseismicity is dependent on the regional stress state and the heterogeneity of the stimulated domain. If the permeability is calculated from the strains developing in the reservoir during the treatment, permeability will increase mostly in the areas of active fracturing. The next step is to fully couple the poroelasticity and the geomechanical response and to fit the model to the engineering parameters during the treatment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".