A Novel Plunger Data Analysis Methodology

Abstract

Abstract A novel methodology for conventional plunger data analysis to evaluate the impact of multiple operational setpoint configurations on gas production is presented. The algorithm first interprets the time signature data of gas flow rate, tubing, casing, and line pressures from a well by recognizing and counting the plunger cycles. Afterward, it breaks the data cycle-wise by recognizing the conventional plunger lift stages: buildup, upstroke, and after flow. Once the algorithm organizes all the data, it normalizes and groups the different stages towards obtaining averaged stage curves. Stage-to-stage dependency is also evaluated from a data-driven perspective. The average normalized stage curves and stage-to-stage dependency evaluation are then used to build synthetic cycles at a given off-stage (time the well is shut-in) and on-stage (time the well is open) set point configuration. Multiple synthetic setpoint configurations are then explored, yielding gas flow rate responses that are used to create an operational map that can be used to optimize the well’s operation. The overall methodology is fully data-driven, therefore, operational constraints such as plunger fall time and required pressure for a full plunger upstroke trip are not predicted.