Mechanisms and Optimization of Critical Parameters Governing Solid-Phase Transport in Jet Pumps for Vacuum Sand Cleanout
Bibliographic record
Abstract
This paper addresses the critical challenge of insufficient solid-phase suction capacity in jet pumps during vacuum sand cleanout operations for low-pressure oil and gas wells. Through integrated numerical simulations validated by experimental measurements with under 15% error, a kind of nonlinear interaction mechanism among key operational and solid-phase parameters is revealed in this paper. The results demonstrate that due to intensified turbulent dissipation, particle diameters exceeding 0.5 mm will lead to a significant decrease in pump efficiency, while an increase in solid volume fraction can improve the solid transport rate but will reduce the energy conversion efficiency. Working pressure optimization shows that the pump efficiency will reach its maximum when the work pressure is 5 MPa, while if it is 8 MPa, the solid transport capacity will be increased by 116%. A discharge pressure exceeding 2.5 MPa will reduce the suction pressure difference and disrupt solid phase transport. A novel dual-metric framework considering the solid transport rate and pump efficiency is put forward in this paper, which includes limiting the particle diameter to 0.5 mm or less, maintaining a solid volume fraction below 30%, and keeping the working pressure between 5 and 8 MPa and the discharge pressure at 2.5 MPa or lower. This method can increase the sand removal efficiency to over 30% while minimizing energy loss, providing a validated theoretical basis for sustainable wellbore repair in depleted oil 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 | 0.001 | 0.001 |
| 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".