Dynamic Capillarity During the Water Flooding Process in Fractured Low Permeability Reservoirs
Bibliographic record
Abstract
Abstract Capillary pressure and relative permeability are the two main factors determining the multiphase flow in oil and gas reservoirs. Dynamic capillarity, which includes the dynamic capillary pressure and the dynamic relative permeability, should be considered when performing waterflooding in low permeability oil reservoirs. To stimulate the production, hydraulic fracturing has been applied in low permeability oil reservoirs. In this work, dynamic capillarity in fractured low permeability reservoirs were investigated through numerical simulation, which applied the capillary pressure and relative permeability data obtained from steady and dynamic waterflooding experiments. The numerical simulation conducted sensitive analysis using CMG. The results show that if the steady data are used in the prediction, the oil saturation reduces more evenly and more quickly, and the production capability of the reservoir is overestimated. Moreover, the production well will be predicted to breakthrough earlier, with a higher breakthrough water flow if the dynamic capillarity is neglected This work demonstrates the importance of considering dynamic capillarity in fractured low permeability reservoirs, and provides another perspective to predict the production in fractured low permeability reservoirs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".