Prediction of gas flow rates from gas condensate reservoirs through wellhead chokes using Differential Evolution Metaheuristic Algorithm

Abstract

The prediction of gas flow rates through wellhead chokes in condensate reservoirs involves considering various factors, including the properties of the hydrocarbon gas mixture, the choke design and specifications, reservoir conditions, and the interaction between the gas and natural gas liquids. These complex interactions influence the flow regime, pressure drop, and overall system behavior, making prediction a challenging task, the Researchers and engineers have developed various mathematical models, empirical correlations to predict gas flow rates through wellhead chokes in condensate reservoirs. These models often consider fundamental fluid mechanics principles, such as conservation of mass and energy, combined with empirical data from field measurements. The proposed models usually require a feasible parameter extraction of the unknown coefficients. This identification process could be achieved by means of an optimization method. Differential Evolution (DE) is one of the effective evolutionary algorithms used to solve global optimization problems in different domain. The basic concept of DE algorithms is built by three strategies: Mutation, Crossover and Selection. This study shows the importance and strength of this meta-heuristic technique. By using real datasets from two different wells and two different models, the findings by using DE algorithm have been compared with the results reached by means of ACO algorithm and others from a recently published paper