Wellhead Choke Performance for Multiphase Flowback: A Data-Driven Investigation on Shale Gas Wells

Wellhead choke performance is critical for flowback choke-size managements in unconventional gas wells. Most existing empirical correlations were originally developed for oil and gas flow, and their accuracy for gas/water multiphase flowback remains uncertain. This study presents a data-driven approach to examine the choke–performance relationship during multiphase flowback. We compiled a flowback dataset containing 18,660 surface measurements from 37 shale gas wells in the Horn River Basin. Using machine learning, we modeled choke performance based on flowback features including water rate, gas/water ratio, wellhead and separator pressures and temperatures, and choke size. The models achieved strong predictive accuracy. Based on the machine learning results, we developed a new choke–performance correlation tailored to multiphase flowback. This model was validated against field data and showed reliable performance. The findings provide a useful tool for optimizing choke-size strategies during flowback in hydraulically fractured gas wells, especially in unconventional reservoirs.