Where is the Proppant? DFN Modeling, Proppant Placement, and Drainage Estimation from Microseismic Data
Bibliographic record
Abstract
Summary Microseismic data for 17 hydraulically stimulated wells in the Marcellus Shale were used to conduct a proppant placement and drainage estimation evaluation in order to optimize the wellbore spacing, and in the future the completion design and landing depth of the wellbores. A calibrated discrete fracture network (DFN) was modeled onto the microseismic events taking into account the magnitude of the events, their source mechanisms, the rock properties of the stimulated reservoir, and the amount of injected fluid in order to distinguish between the total stimulated rock volume (SRV) where microseismic activity was observed and the part of the SRV that contains proppant filled fractures and will therefore be productive in the long term. Evaluating proppant placement in the perpendicular horizontal, parallel horizontal, and perpendicular vertical direction has significance for and direct impact on wellbore spacing, stage length and spacing, and the landing depth of the wellbores.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".