Feasibility and Evaluation of Surfactants and Gas Lift in Combination as Severe Slugging Suppression Method
Bibliographic record
Abstract
Abstract An experimental study of severe slugging suppression utilizing a combination of surfactants and gas-lift was conducted using a facility consisting of a 3-in. ID, 65 ft long, -3° inclined flow line 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. Pressure behavior in the flow line – riser system was monitored and input gas, injection gas, liquid and surfactant flow rates were continuously measured. In addition, visual observations were made to identify severe slugging. Effects of the proposed method were quantified with a modified elimination performance index (MEPI) considering not only pressure fluctuations but also back-pressure effects. A total of 30 tests were conducted. The data were analyzed for the severe slugging suppression of the surfactant and gas-lift combination, 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 the all of the tests conducted. Surfactants were able to suppress severe slugging for most of the cases. The performance of "Only Surfactant Injection Case" increases as the gas-liquid ratio increases. For all of the tests, back-pressure reduction was observed. MEPI is used as the main parameter to assess the performance of the severe slugging suppression methods. Gas-lift not only contributes with 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 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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".