Plunger lift system installations throughout the United States are experiencing an alarming boom as liquid loading becomes an ever-increasing impediment to hydrocarbon recovery in depleting wells.

Newly installed plunger lift installations within a field normally required individual onsite calibration at each well site. Accurate calibration of these installations can take many hours or even days to ensure optimal capability to recover liquid within the tubing string at acceptable rates. Operating expenses in turn often increase reflecting the time needed on location to "tune" the system to levels approaching maximum operational efficiency under plunger lift. Given the increase in the number plunger lift installations within a field(s) and resulting limitations in onsite operator calibration time at each installation, the need for automation to aid in "efficient tuning" of each individual plunger lift installation is critical to maintaining a steady production decline in a well with liquid loading characteristics.

Today, with the advent of modern automation technology, it is possible to access and manage all plunger lift installations within a producing field remotely. This can be accomplished with the aid of a personal computer connected to the field's collective network of plunger lift controllers via the use of a SCADA (Supervisory Control and Data Acquisition) system supported by key control system components such as Programmable Logic Controllers ("PLC") and Remote Terminal Units ("RTU"). These provide a field operator with the capability to perform basic operational tasks (e.g. the closing and opening of valves), without being present at the well site and allow for the plunger lift installation to adapt to changing field conditions without operator interface.

This paper will address the proper application of Smart Plunger Diagnostic Technology in the field with the objective of increasing plunger lift installation efficiency in turn leading to improved well productivity with minimal increased operating expense.

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