Linking Reservoir Simulators with Fracture Simulators
Bibliographic record
Abstract
Abstract Reservoir simulation is currently used as the primary technique to simulate the behavior of almost all types of hydrocarbon reservoirs. Although several techniques have been developed to simulate the behavior of hydraulic fractures in reservoir simulators, a lack of accurate modeling of fracture geometry and fracture-fluid leakoff, as well as some other effects on hydraulic fractures (e.g., non-Darcy flow, dynamic fracture-conductivity behavior, stress-permeability dependence), is commonly observed in commercial-reservoir simulators. A different approach is presented in this paper to fill the gap between reservoir simulators and hydraulic fracture simulators. Software capabilities have been developed to import propped-fracture geometry, proppant-area concentration, and fracture-fluid leakoff from commercial, grid-based fracture simulators, into a reservoir simulator with capabilities to model multiphase, non-Darcy flow inside the fracture. This includes the effects of long-term dynamic conductivity, stress-permeability dependence, and condensates banking, etc. This functionality permits better modeling of fracture-flow behavior and gives better insight into the cleanup and productivity of fractured wells, which will in-turn allow the user to design better fractures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".