Performance evaluation of Hybrid Parallel Genetic Algorithm for solving flow rate reconciliation

To deal with the problem of flow rate reconciliation in deepwater natural gas-condensate production system, master-slave-coarse grained Hybrid Parallel Genetic Algorithm (HPGA) was adopted in this paper to estimate the single-well flow rate so as to cover the defects of Simple Genetic Algorithm (SGA), i.e., long time consumption for computation. Based on distributed storage of multi-core PC cluster system, HPGA is realized by means of thread and process parallelism. Specifically, Master-Slave Genetic Algorithm (MSGA) is applied within one node, and Coarse-Grained Parallel Genetic Algorithm (CGGA) is adopted among several nodes. Then, case study was carried out on the production systems of two wells in a certain gas field. The parallel performance of HPGA when being applied in virtual flow metering was studied by comparing the computation time and calculation results of HPGA, MSGA and SGA. It is showed that for HPGA, the parallel efficiency and the proportion of speedup ratio to linear speedup ratio are both over 70%. Meanwhile, the computation time is significantly reduced and the error of flow rate estimate is decreased, which satisfies the needs of offline analysis on engineering operation. In addition, the variation of speedup ratio and parallel efficiency with the number of processes and populations was studied so as to discuss the effect of parallel overhead.