Modeling and Optimization of Gas-Assisted Plunger Lift GAPL by a Transient Simulator: A Case Study of a Permian Shale Well

Abstract

Abstract Gas-assisted plunger lift (GAPL) could be an effective and economically favorable artificial lift (AL) method to be considered during the AL life cycle for North American shale wells. The main advantage of GAPL is that it improves the well production by reducing liquid fallback and boosts the plunger efficiency through gas injection and increases the gas lift efficiency by assisting in delivering the slugs to the surface. The objective of this study is to capture the GAPL dynamic behavior through a transient multiphase flow simulator. The entire GAPL production cycle was modeled, including plunger fall, gas injection, pressure buildup, and production. First, the GAPL well production history was analyzed to evaluate the well operating condition. Then, a transient simulator was used to model the well flow behavior and production performance with GAPL. The study demonstrated the GAPL impact on flowing bottomhole pressure and the improvement in the well productivity. A Delaware Basin well case study demonstrates the benefits of dynamic modeling and provides a comprehensive comparison between dynamic simulation results and field data. The simulation work provides insights into the fluid flow, GAPL behavior, and pressure and rate transients of a GAPL well. The modeling results were validated against field data. A commercially available transient multiphase flow simulator was used and produced outcomes that were in alignment with field data collected. The dynamic plunger cycles were reproduced in the simulation, and the results showed the benefits of GAPL in a typical shale oil well. This could extend the gas lift life by delaying the transition to rod pumps or potentially act as an end-of-life AL solution. In the long term, this reduces the overall AL life cycle cost. The use of transient simulation helps validate AL design concepts, especially for unconventional wells where the flow behavior is very dynamic. This study encourages the use of this analysis in the AL selection workflow to help optimize the overall AL life cycle cost and maximize the net present value (NPV).