Development of an Algorithm of Dynamic Gridding for Multiphase Flow Calculation in Wells
Bibliographic record
Abstract
Summary Because more and more wells have been put in operation, an accurate modelling of wellbore flow plays a significant role in reservoir simulation. One requirement of a wellbore model is its ability to trace various flow boundaries in the tubing, such as those created by phase or flow regime changing. An algorithm of dynamic gridding is applied to the wellbore flow model coupled with Stanford?s general purpose research simulator (GPRS), which has the capability to simulate the isothermal black oil reservoir model to obtain detailed information that explains such important quantities as flow pattern and mixture velocity in any specific location of wellbore. A significant problem in this case is how to calculate fluid and velocity properties with a fine grid (segment) on the boundaries of different flow regimes in the wellbore. Local dynamical segment refinement in the well can accurately and effectively handle this problem. This wellbore model includes mass conservation equations for each component and a general pressure drop relationship. The multiphase wellbore flow is represented using a drift-flux model, which includes slip between three fluid phases. The model determines the pressure, mixture flow velocity, and phase holdups as functions of time and the axial position along the well or alleviation depth. In addition, this model is capable of generating automatically adaptive segment meshes. We apply the black oil model to the simulation of several cases of isothermal dynamical local mesh refinement, and compare the results with fixed coarse and fine meshes. The experiments show that using local segment refinement can yield accurate results with acceptable computational time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".