Improving permeability estimation of carbonate rocks using extracted pore network parameters: a gas field case study

Abstract

Despite numerous studies carried out on permeability estimation from either 2D/3D images or models, a precise evaluation of the permeability for carbonate rocks is still a challenging issue. In this study, the capability and advantages of pore network parameters extracted from 2D thin-section images as inputs of intelligent methods for permeability estimation of carbonate rocks are explored. Pore network extraction in image processing is an effective approach for microstructure analysis. A physically practical pore network is not just a portrayal of the pore space in the context of both morphology and topology, but also a valuable instrument for predicting transport properties precisely. In the current research, a comprehensive workflow was first presented to extract the pore network parameters from a set of core thin-section microscopic images from the carbonate reservoir rock of the South Pars gas field located in the southern borders of Iran. Subsequently, an artificial neural network (ANN) model was designed to predict the permeability of the considered samples using the extracted pore network parameters. To highlight the efficiency of the proposed approach, the second ANN model was implemented to estimate the permeability of the samples using the conventional well log data. The quantitative comparison of the obtained results using both ANN-based models reveals a significant enhancement in the predicted permeability through the extracted pore network parameters.