Permeability prediction using Probabilistic Neural Network (PNN): Application to the Paleozoic shallow marine sandstone of Quwarah member, Qasim Formation, Saudi Arabia

This outcrop analog study was conducted on surface equivalent to the Quwarah member of the middle to late Ordovician of Qasim Formation in central Saudi Arabia. The Paleozoic section contains important oil and gas reservoirs with more to explored and developed mainly related to unconventional tight gas and shale gas. The main objectives of this work is to use the probabilistic Neural network (PNN) to predict permeability of the Quwarah sandstone on the basis of systematically collected and petrographically estimated textural and compositional data from the outcrop sections of the Quwarah member. The results show that probabilistic neural network (PNN) was capable of reproducing permeability with very high accuracy, so that the calculated correlation coefficient for permeability was 0.89. This outcrop analog study, when integrated with subsurface data, might provide database, reveals heterogeneity and enhances understanding and better prediction of reservoir quality in the subsurface.