Estimating permeability from hydraulic fracturing induced microseismicity
Bibliographic record
Abstract
Hydraulic fracturing is often performed to enhance the permeability of hydrocarbon bearing shales and tight sands. The microseismic event clouds accompanying the fracturing operations have been used for permeability estimation by attributing the triggering front observed in the distance versus time plots to pore pressure diffusion. We show that in low-permeability hydrocarbon reservoirs like shales and tight sands, the growth rate of hydraulic fracture can be much faster than that of the pore pressure diffusion front. Therefore, the observed triggering front in such cases may be attributed to crack tip propagation. Moreover, equating the triggering front to the diffusion front yields highly overestimated diffusivity values. We propose to use the width, instead of the length, of the microseismic cloud to get a better diffusivity estimate. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 8:30 AM Presentation Start Time: 11:25 AM Location: 303B Presentation Type: Oral
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".