Experimental, Numerical, and Statistical Investigation of Two Phase Flow in a Cylindrical Perforation Tunnel
Bibliographic record
Abstract
Abstract Perforation is the final stage in well completion that helps to connect reservoir formations to wellbores during hydrocarbon production. The drilling perforation technique maximizes the reservoir productivity index by minimizing damage. This can be best accomplished by attaining a better understanding of fluid flows that occur in the near-wellbore region during oil and gas operations. The present work aims to enhance oil recovery by modelling a two-phase flow through the near-wellbore region, thereby expanding industry knowledge about well performance. An experimental procedure was conducted to investigate the behavior of two-phase flow through a cylindrical perforation tunnel. Statistical analysis was coupled with numerical simulation to expand the investigation of fluid flow in the near-wellbore region that cannot be obtained experimentally. The statistical analysis investigated the effect of several parameters, including the liquid and gas flow rate, liquid viscosity, permeability, and porosity, on the injection build-up pressure and the time needed to reach a steady-state flow condition. Design-Expert® Design of Experiments (DoE) software was used to determine the numerical simulation runs using the ANOVA analysis with a Box-Behnken Design (BBD) model and ANSYS-FLUENT was used to analyses the numerical simulation of the porous media tunnel by applying the volume of fluid method (VOF). The experimental data were validated to the numerical results, and the comparison of results was in good agreement. The numerical and statistical analysis demonstrated each investigated parameter’s effect. The permeability, flow rate, and viscosity of the liquid significantly affect the injection pressure build-up profile, and porosity and gas flow rate substantially affect the time required to attain steady-state conditions. In addition, two correlations obtained from the statistical analysis can be used to predict the injection build-up pressure and the required time to reach steady state for different scenarios. This work will contribute to the clarification and understanding of the behavior of multiphase flow in the near-wellbore region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".