Wellhead Choke Performance in Oil Well Pipeline Systems Based on Genetic Programming

Abstract

The accurate determination of choke performance in oil pipelines is considered a main challenge for petroleum engineers. This study introduces a new correlation based on genetic programming (GP) to describe choke performance in both critical and subcritical flow conditions in pipeline flow regimes. The GP model is developed using field data collected from various Iranian oil well pipelines. Statistical error parameters are used to compare the accuracy of results obtained from the new correlation with other empirical models. The GP model predicted the choke performance for critical flow condition with relatively lower error (normalized mean square error = 0.006) and higher accuracy (R=0.993%). In addition, the results of the model in a subcritical flow regime showed that the GP model could be applied effectively and yield high accuracy for choke performance pipeline forecasting.