Bibliographic record
Abstract
Abstract Effective propped fracture half-lengths following a typical hydraulic fracture stimulation of a wellbore can be difficult to quantify. Therefore, new modeling techniques must be developed to make estimates of proppant distribution in a formation to understand the spatial extent of proppant-filled fractures. The distribution of propped and unpropped fractures can then be statistically analyzed to determine the productive stimulated rock volume (SRV) and constrain key field development parameters such as wellbore and stage spacing as well as vertical containment of proppant placement. A magnitude-based calibrated Discrete Fracture Network (DFN) methodology based on microseismicity induced during stimulation of a wellbore has been developed that incorporates magnitude of the event (and associated microseismic moment (M)), rock rigidity (μ), injected fluid volumes (Vi), and fluid efficiency (η). Calculated fracture volumes (Vf) are then scaled to account for any missing portion of the seismic population. The calibrated DFN can then be filled with the measured injected proppant volume on a stage-by-stage basis by initially filling fractures nearest the wellbore and systematically filling fractures outward from the wellbore until all deposited proppant volumes have been depleted. Fundamentally, for every microseismic hypocenter, fracture area (A) =M/μδ where δ is displacement along the slip plane. Since δ is not directly measured, it is initially estimated using an empirical relation as a function of M and corrected using a scaling factor (k) where k=Vf/[(Vi)η]. The scaling factor is calculated by comparing Vf to (Vi)η following a hydraulic fracture stimulation of an individual stage where the sum of the seismic moments is greatest (compared to all stages monitored) and energy released is only associated with fluid injection (e.g. not tectonic activity).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".