Feasibility and Evaluation of Surfactants and Gas Lift in Combination as a Severe-Slugging-Suppression Method
Bibliographic record
Abstract
Summary An experimental study of severe-slugging suppression by use of a combination of surfactants and gas lift was conducted with a facility comprising a 3-in.-inner-diameter, 65-ft-long, -3°-inclined flowline, followed by a 45-ft-long vertical-riser system. Air and water were used as fluids. The surfactant used was a foaming agent capable of forming stable foams in all brines for a wide range of pH values. Pressure behavior in the flowline/riser system was monitored, and input-gas-, injection-gas-, liquid-, and surfactant-flow rates were measured continuously. In addition, visual observations were made to identify severe slugging. Effects of the proposed method were quantified with a modified elimination performance index (MEPI) that considered not only pressure fluctuations, but also backpressure effects. Thirty tests were conducted. The data were analyzed for the severe-slugging suppression of the combination of surfactant and gas lift, the effect of gas lift on surfactant injection, and the effect of the surfactant on the reduction of the gas lift gas. The combination technique with the highest gas lift rate completely eliminated the severe slugging for all tests conducted. Surfactants were able to suppress severe slugging for most of the cases. The performance of the ’only-surfactant injection case’ increases as the gas/liquid ratio increases. For all of the tests, backpressure reduction was observed. The MEPI is used as the main parameter to assess the performance of the severe-slugging-suppression methods. Gas lift not only contributes to density reduction through volumetric increase of gas in the riser, but it also reduces the mixture density by promoting more foam generation. There were reductions in the gas lift rate from the original maximum gas lift injection rate for all the tests conducted with surfactant injection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 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".