A Study of Fluid-Flow Mechanism for Cold Production With Sand and Single-Well Fine Numerical Simulation in Heavy-Oil Reservoirs
Bibliographic record
Abstract
Abstract In order to study the fluid flow mechanism for cold heavy oil production with sand (CHOPS) and predict their production performance precisely, criteria for the skeleton sand erosion and movable sand startup have been set up, based on the stress analysis on formation rock and sand particulates. According to the principles of effective stress, mass conservation, virtual displacement, equivalent virtual work and so on, both the single-well-black-oil model of CHOPS with three dimensions and four phases and the equilibrium equation of borehole wall rock with elasto-plastic deformation have been established. Considering the basic definitions of physical property parameters and the effect on physical property parameters caused by bulk strain, skeleton sand erosion, movable sand deposition on the pore surfaces and movable sand bridge plug at pore throats, dynamic models of physical property parameters have been established for numerical simulation of CHOPS. Then, the coupling solutions of the model have been studied. Finally, a case to verify the correctness and validity of the model is simulated and analyzed. The establishment of the mathematic model describing the fluid flow mechanism of CHOPS is of great significance to help us recognize correctly and exploit heavy oil reservoirs efficiently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".