Increasing Production via Foam Assisted Gas Lift in a Mature Oil Well
Bibliographic record
Abstract
Abstract Continuous Foam Injection is a proven deliquification technique in gas wells, but the technology typically struggles to perform in wells with high fractions of liquid hydrocarbons. For gas-lifted oil wells operating at high water cut, continuous downhole foam injection via the gas-lift system may prove feasible and open-up a whole new area of production enhancement. To establish if this technique could deliver sustainable and cost effective production enhancement in the field, Shell Malaysia Exploration & Production (SMEP) successfully conducted a trial in October 2016 in a mature oil field where a liquid foamer was injected into the gas lift system of an oil well. The project team took 10 months to conduct candidate well screening, comprehensive lab testing for chemical selection, well performance modelling, procurement, site visit, plant change requirements and finally site execution. Although the candidate well was located in an aging facility with limited production monitoring facilities, the available surface pressure/temperature transmitters and fluid sample points were sufficient to ensure a robust assessment of the trial results was possible. Over the trial period, a 22% increase in the gross production rate was seen in the candidate well, with downhole foamer and surface defoamer being applied on a continuous basis. Throughout the trial, the fluid properties were closely monitored to ensure secondary effects such as excessive surface foaming or untreatable emulsification did not occur and this strategy proved successful with the trial being completed without any downstream system upset. The trial being described in this abstract was the first time a foamer had been applied in this manner in Shell Malaysia.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".