Rigorous Modelling of Fractures in a Porous Medium
Bibliographic record
Abstract
Abstract While fractured formations are possibly the most important contributors to the oil production worldwide, modelling fractured formations with rigorous treatments has eluded reservoir engineers in the past. To-date, one of the most commonly used fractured reservoir model remains the one that was suggested by Warren and Root more than three decades ago. In this paper, a new model for fractures embedded in a porous medium is proposed. The model considers the Navier Stokes equation in the fracture (channel flow) while using Brinkman equation for the porous medium. Unlike the previous approach, the proposed model does not require the assumption of orthogonality of the fractures (sugar cube assumption) nor does it impose incorrect boundary conditions for the interface between the fracture and the porous medium. The proposed model is derived through a series of finite element modelling runs for various cases using Navier Stokes equation in the channel while maintaining Brinkman equation in the porous medium. Various cases studied include different fracture orientations, fracture frequencies, fracture width, and the permeability of the porous medium. Finally, a series of numerical runs also provided validity of the proposed model for the cases for which thermal and solutal effects are important. Such a study of double diffusive phenomena in the context of fractured formations has not been reported before.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".