Study of fracture-induced anisotropy from discrete fracture network simulation of well test responses
Bibliographic record
Abstract
Abstract Different conceptualizations of fracture networks have been generated in order to simulate well tests in fractured reservoirs. These models have two purposes: firstly to investigate whether the behaviour of realistic fracture networks can be emulated with more simple models; and secondly to assess whether different sources of anisotropy have characteristic and recognizable influences on the pressure derivative. The anisotropy of the fracture networks was increased by decreasing the angle between two originally orthogonal fracture sets, and by increasing the permeability contrast between two orthogonal fracture sets. Results indicate that simple models can capture the first-order behaviour of more realistic examples. However because early time data are strongly influenced by the connectivity of the fracture network, the degree of anisotropy can only be assessed at later times in a test. Increasing anisotropy results in increasing heterogeneity and compartmentalization, and permeability anisotropy in an orthogonal system can only partially replicate the behaviour of a geometrically anisotropic system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".