Incorporating Microseismic data into Discrete Fracture Network Models in Enhanced Geothermal Systems, Utah FORGE
Bibliographic record
Abstract
Summary The Utah Frontier Observatory for Research in Geothermal Energy (FORGE) is a multi-year initiative funded by the US Department of Energy for testing targeted EGS research and development. The authors have been supporting the FORGE project since 2017 to develop Discrete Fracture Network (DFN) models to characterize both the natural fractures present and the induced fractures created during hydraulic stimulation. Previous DFN models of the site included hundreds to thousands of discrete fractures and relied on stochastic generation of features. This new simplified DFN model is created to provide an alternative fracture network having fewer discrete features and potentially captures the most significant flow pathways. This DFN is being used for further modelling of the long-term thermal and mechanical evolution of flow paths between these wells. The workflow for defining fractures from microseismic points is presented along with a comparison of fracture sizes and orientations between these discovered features and the fracture sets used in previous DFN models for the site.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".