A Streamline Based Lagrangain Method to Investigate Two-Phase Flow in Hydrocarbon Recovery
Bibliographic record
Abstract
Abstract The study of miscible flow is an important topic in the field of petroleum science and engineering. The miscible displacement of hydrocarbon-like oil by a solvent such as carbon dioxide helps to enhance the recovery of the hydrocarbon. The use of CO2 helps to reduce the viscosity of resident fluids like oil, In addition, the use of CO2 would reduce its accumulation in the atmosphere to help a green environment. The production of hydrocarbons in the secondary or tertiary phase follows the mostly miscible flow. The Computational Fluid Dynamics (CFD) simulations can help with better understanding enchanch oil recovery in the two-phase flow system. The traditional simulations of such flow suffer numerical artifacts due to the appropriate methods. We present a generalized CFD model for studying the transient methodology for momentum transfer. We investigate an upscaling approach to resolve the small-scale features of miscible flow in a porous medium. A streamline-based Lagrangian model is developed to study the displacement of oil by CO2 with sufficient accuracy and near-optimal computational cost.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".