Plunger Lift operations are oftentimes not optimized due to lack of knowledge of plunger location and changes in tubing pressures, casing pressures and bottomhole pressures. Monitoring the plunger location in the tubing helps the operator to optimize the production of liquid and gas from the well.

In low liquid volume wells, the plunger position can be tracked from the surface by monitoring acoustic signals generated as the plunger falls down the tubing. When the plunger falls through a tubing collar recess, an acoustic pulse is generated. These acoustic pulses, generated at the tubing collar recesses, travel through the gas in the tubing and can be monitored at the surface to obtain plunger depth. These acoustic pulses are converted to an electrical signal by use of a microphone or pressure transducer. The signal is digitized, stored and processed in a computer to determine plunger depth. In some high liquid volume wells, the acoustic pulses generated as the plunger falls past the tubing collar recesses may be masked and not detectable due to liquid accumulation around the plunger. However, in both low and high liquid volume wells, the plunger depth can be determined by generating an acoustic pulse in the tubing at the surface, and monitoring the acoustic reflection from the top of the plunger. Multiple shots are taken, so the plunger descent rate can be determined throughout the plunger fall.

Software processes this plunger depth data along with the tubing and casing pressure data to display plunger depth, plunger velocity and well pressures vs. time. Plunger arrival at the liquid level in the tubing, and plunger arrival at the bottom of the tubing are identified on the data plots. Well inflow performance is calculated and plotted.

Software displays the data and analysis in several formats including a pictorial representation of the well showing the tubing and casing pressures, plunger location, gas and liquid flow rates in the tubing and annulus, and inflow performance relationship at operator selected intervals throughout the cycle. A field case is presented to show how this field data analysis is applied to optimization of Plunger Lift operations.

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