Comparison of deterministic with stochastic fracture models in water-flooding numerical simulations
Bibliographic record
Abstract
Abstract Determination of multiphase flow properties considering the variation of fracture patterns (i.e., number of fracture sets, their orientation, length distribution, spacing, and in-situ aperture) remains a key challenge in reservoirs. In reservoir engineering, one way is by studying outcrop analogs with comparable petrophysical properties and a similar geological history, and incorporating these data into model building, discretization, and numerical simulation. The limitation of directly incorporating attributes measured on outcrops is that this method is error prone because of postburial processes. Mineralized fracture (vein) attributes are good candidates to use as analogs for open fractures formed under in-situ conditions, to establish the relationship between fracture length and aperture and help to reveal the conditions at the time of their formation, and to quantify fracture-induced porosity in rock masses. Vein attributes determined from scan lines and window samples were combined to condition the stochastic generation of fractures using the discrete fracture network code FracMan. Comparison of water breakthrough time and oil saturation at breakthrough was then determined by applying a constant pressure gradient for each realization to simulate water-flooding numerical simulation using the combined finite element–finite volume method. The different stochastic realizations were compared with discrete fracture and matrix models, and we show how the uncertainty in these fracture attributes affects multiphase flow behavior in naturally fractured rocks. Uncertainty in quantifying these attributes has a profound impact for predicting the oil recovery and water breakthrough time based on limited information from boreholes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".