A new development algorithm for permeability prediction: A new milestone

Permeability is one of the most important reservoir rock parameters in petroleum engineering, reservoir, and exploitation. This parameter causes movement hydrocarbon reserves rock. Therefore, it an from economic point view because greatly impacts amount extraction In this study, combined RBFNN-GA algorithm 200 data sets collected a field Middle East were used to predict permeability. Water saturation, porosity, specific surface are input variables study. GA has advantages such as solving complex optimization problems continuous functions multi-objective problems. The RBF neural networks that they easy design, strongly tolerant noise, have good generalization. model both algorithms. experimental models been compared terms performance accuracy. results show with STD = 89.8 R -square 0.9011 for total set obtained better accuracy predicting permeability than models. Compared other network methods, higher efficient parameters. Oil researchers engineers can use method their studies research.