Plunger Lift Optimization in Horizontal Gas Wells: Case Studies and Challenges
Bibliographic record
Abstract
Abstract Plunger lift is a proven technique for lifting liquids from vertical gas wells flowing below critical velocity. The use of plunger lift in horizontal gas wells was not initially expected to provide any difficulties or challenges. While it has been relatively easy to make plunger lift function in horizontal wells, truly optimizing production has proven to be much more challenging. One of the challenges of horizontal wells is moving liquids from the horizontal well bore to the tubing above the bumper spring assembly. To facilitate this bumper springs are landed as deep as possible, which creates operating challenges for both bumper springs and plungers. Bumper spring check valve limitations were identified at high deviation angles and re-designed bumper springs have been deployed to address this. Recent testing has shown that the efficiency of some plunger types can be significantly reduced when used in horizontal wells and the explanation for this decrease as well as the test results are provided. Plunger control software development for the Sierra field's horizontal well application has led to increased production times and volumes. It has resulted in additional operational benefits that reduce down time caused by cold weather and pipeline pigging operations. The software is embedded into the SCADA control system and can utilize well parameters monitored within SCADA to vary the plunger control parameters. This paper presents case studies showing the impact of plunger control software for horizontal wells. Case studies detailing operational performance benefits and the performance of various types of plungers in high angle applications are also presented. Additional case studies show the impact on plunger performance of well bore trajectory and liquid hold up in the horizontal well bore. Continuous improvements to strategies and operations have been implemented to fully optimize horizontal gas wells.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".