Measured Plunger-Fall Velocity Used To Calibrate New Fall-Velocity Model
Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 164495, ’Measured Plunger-Fall Velocity Used To Calibrate New Fall-Velocity Model,’ by O.L. Rowlan and J. McCoy, Echometer Company; J. Lea, PLTech; and R. Nadkrynechny and C. Cepuch, T-RAM Canada, prepared for the 2013 SPE Production and Operations Symposium, Oklahoma City, Oklahoma, USA, 23-26 March. The paper has not been peer reviewed. Fall velocities for various plungers have been measured under many different field and simulator conditions. A new theoretical plunger-fall-velocity model uses a specific pressure and temperature for calibration. The model can then be used to calculate fall velocity at other conditions for the same plunger or can be used to show how changing a feature such as plunger weight can affect fall velocity. Introduction Conventional plunger lift is a low-cost method for lifting liquids (water, condensate, or oil) from gas and oil wells. Lifting liquids from the well is achieved by closing a surface valve to store energy in the well during a shut-in time period, which is followed by opening the surface valve for a time period so liquids are unloaded as gas flows to the surface. During shut-in, the gas flow is stopped when the controller closes the surface motor valve. The plunger leaves the lubricator to begin its fall from the surface because of a tubing-pressure increase that is caused by closing the motor valve or begun when the plunger is released from a catcher. The plunger falls through gas until entering the accumulated liquid at the bottom of the tubing. Once the plunger is on bottom and sufficient unloading energy is stored, the controller opens the surface valve into the lower-pressure flowline. High-pressure gas in the tubing above the liquid column flows down the flowline, and the high-pressure gas in the casing begins to decrease by expanding to fill the tubing, displacing the plunger and most of the liquid above the plunger to the surface. This plunger-operation cycle is repeated continually to produce the well. An operator can produce from the well efficiently if the plunger’s fall rate and location and the time taken to fall to the liquid and bottom of the tubing are known accurately. The distance to the plunger and the rate of fall can be determined by examining the acoustic signal created by a falling plunger. The acoustic pulse generated at the tubing-collar recess travels through the gas to the surface to be detected by a microphone, and the change in pressure can be detected by a tubing pressure transducer. These acoustic pulses are normally detected as a plunger falls down the relatively dry tubing interior above the gaseous liquid column at the bottom of the well. Processing this acoustic signal allows the depth and fall velocity of the falling plunger to be determined. Fig. 1 shows the plunger-fall velocity decreasing smoothly as a function of time. Although there seems to be some scatter of velocities on the plunger-velocity trace, note that the left vertical scale is amplified and that the general trend of the velocity is to consistently decrease as time (plunger depth) increases.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".