WELL REPRESENTATION IN RESERVOIR SIMULATION MODELS CONSIDERING THE IMPACT OF DISCRETE FRACTURE NETWORK UPSCALING
Bibliographic record
Abstract
The goals of this work are (1) to evaluate the impact of Discrete Fracture Networks (DFN) upscaling methods on new wells placed in the simulation model and (2) to develop a well representation proposal of new wells in naturally fractured reservoirs simulation models. Three DFN permeability upscaling methods (Oda, Oda Corrected, and Flow-based) are used at three model scale fidelities (high, medium-high, and medium). The results suggest more uncertainty of well-dynamic data in medium fidelity models. Our reference case is defined as the combination of fidelity scale and upscaling method that produces less variation in well-dynamic data. This results in a model constructed with a high-fidelity scale and Flow-based method (linear pressure). The proposed well representation suggests substituting the well index in the matrix and fracture systems of the medium-fidelity model with the reference model’s WI. We show the necessity of this correction with a field-scale application.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".