A Method To Implement Permeability Anisotropy Associated With Fault Damage Zones in Reservoir Simulation
Bibliographic record
Abstract
Summary In this study, we present a method to incorporate the effects of fault damage zones (DZs) in a reservoir-simulation model. Permeability anisotropy associated with fault DZs depends on many factors, including the geometry of the faults in the reservoir and the associated dimension and density of fractures in the DZs. To model permeability anisotropy caused by fault DZs, we start by using geomechanically constrained discrete fracture models of DZs. Then, we use the orientation and size of the faults with reference to the grid axes to incorporate the effect of permeability anisotropy in the simulation grid. In this case study, in which faults are formed in an extensional regime, DZs show increased permeability along the strike of the fault and in the vertical direction, but there is no significant change in the permeability perpendicular to the faults. Inclusion of DZs in the simulation model shows significant improvement in the history matching in comparison to a base reservoir-simulation model with no DZs. Further, we analyze the uncertainty of the DZ modeling in the reservoir simulation by simulating multiple equiprobable models.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".