Prediction of gas flow rates from gas condensate reservoirs through wellhead chokes using Shuffled Complex Evolution Algorithm

Abstract

To predict gas flow rates through wellhead chokes in condensate reservoirs, various factors must be taken into account, such as the properties of the hydrocarbon gas mixture, choke design and specifications, reservoir conditions, and the interaction between gas and natural gas liquids. The flow regime, pressure drop, and overall system behavior can be influenced by these complex interactions, making it challenging to predict them. Various mathematical models and empirical correlations have been developed by researchers and engineers to predict gas flow rates through wellhead chokes in condensate reservoirs. Fundamental fluid mechanics principles, such as the conservation of mass and energy, are often used in these models, along with empirical data from field measurements. The proposed models usually require a feasible parameter extraction of the unknown coefficients. An optimization method could be used to achieve this identification process. The SCE algorithm is one of the effective evolutionary algorithms used to solve global optimization problems in different domain. The SCE algorithm has a three-tier architecture that consists of the following tiers: population, complex and simplex .Hence, the philosophy behind SCE algorithm is to treat the global search as a bottom-up population evolution. This study shows the importance and strength of this meta-heuristic technique. By using real datasets from different wells and two different models, the findings by using SCE algorithm have been compared with the results reached by means of ACO algorithm and others from a recently published paper