A New Model for Reservoirs With a Discrete-Fracture System
Bibliographic record
Abstract
Summary Dual-porosity and dual-permeability models for naturally fractured reservoirs assume that the fractures in the reservoir are connected with each other and uniformly distributed. However, in some cases, the reservoir characteristics exhibit a discrete-fracture system, which means that the fractures might be unconnected and their distribution is not uniform. In this paper, a new computational model is developed to compute the transient-pressure behaviour for reservoirs with a discrete-fracture system. This computational model is based on Laplace transforms. The fluid flow in the fracture system and reservoir are computed separately, and flux and pressure equivalent conditions in Laplace space are applied in the fracture wall to couple the fluid flow in both systems. The results suggest that the pressure response in a reservoir with a discrete-fracture system has three flow regions: fluid flow near the wellbore, fracture-dominated fluid flow, and fluid flow in the matrix away from the fracture. The distance between the fracture and the well, fracture parameters (fracture conductivity and non-Darcy effects), and fracture distribution are the main factors affecting the pressure response. In some particular situations, the fracture-dominated fluid-flow region in the pressure-derivative curve may present two valleys, which has been observed in some field cases (Clarkson 2009). The transient-pressure behaviour of a discrete-fracture system is also compared with that for a composite model. It is suggested that in these two scenarios, the early- and middle-time transient-pressure behaviour may be similar and latetime behaviours are quite different. The model provides a tool for identifying the fracture pattern in a specific reservoir.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".