SMU Data Science Review
Abstract
In this paper, we present an approach to reducing bottom hole plunger dwell time for artificial lift systems. Lift systems are used in a process to remove contaminants from a natural gas well. A plunger is a mechanical device used to deliquefy natural gas wells by removing contaminants in the form of water, oil, wax, and sand from the wellbore. These contaminants decrease bottom-hole pressure which in turn hampers gas production by forming a physical barrier within the well tubing. As the plunger descends through the well it emits sounds which are recorded at the surface by an echo-meter that measures tubing acoustics. We analyze acoustic time series data to determine when the plunger comes to rest at the well bottom. By using the Pruned Exact Linear Time (PELT) algorithm for change point detection and k-means clustering to identify points when the plunger passes a pipe collar, we are able to determine the exact time when the plunger reaches the bottom of the well with a 98% accuracy. By knowing exactly when the plunger reaches the well bottom, operators can reduce plunger dwell time resulting in improved well production.
Recommended Citation
Atakpa, Atsu; Farrugia, Emmanuel; Tyree, Ryan; Engels, Daniel W.; and Sparks, Charles
(2018)
"Improving Gas Well Economics with Intelligent Plunger Lift Optimization Techniques,"
SMU Data Science Review: Vol. 1:
No.
4, Article 4.
Available at:
https://scholar.smu.edu/datasciencereview/vol1/iss4/4
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