Numerical study on gas–liquid two‐phase flow within downhole jet pumps
Bibliographic record
Abstract
Abstract This study addresses the technical bottlenecks of conventional artificial lift technologies under high‐pressure conditions in deep well operations, utilizing CFD methods to investigate gas–liquid two‐phase flow characteristics in downhole jet pumps based on actual operating conditions. The research employs Solidworks for modelling, ANSYS ICEM for mesh generation, and the k‐ε turbulence model for numerical simulation analysis. Experimental results demonstrate that jet pump performance characteristics are primarily influenced by key structural parameters including area ratio, nozzle‐throat gap distance, throat length, and diffuser angle, with area ratio showing the most significant impact on hydraulic efficiency. Through the combination of theoretical design empirical formulas and field production data, along with in‐depth numerical simulation optimization, optimal design ranges were established: nozzle‐throat area ratio of 0.23–0.28, nozzle‐throat gap distance of 2–3 times nozzle diameter, throat length of 7–8 times throat diameter, and diffuser angle of 6°. The scientific validity and reliability of these parameters were verified through systematic comparative analysis of pressure distribution characteristics, velocity field evolution patterns, and turbulent field variation characteristics under different working conditions. This research elucidates the complex flow mechanisms in jet pump operations and establishes a theoretical framework for structural optimization and performance enhancement, thereby contributing to improved deep well lifting efficiency and economic viability of production operations.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".