Pore-scale Reconstruction of Tight Reservoirs Using Generative Adversarial Networks

A significant amount of Iranian hydrocarbon resources is produced from fractured reservoirs with tight rock matrices. The structure of pores in these reservoirs is so complex. Very tiny pores and throats in nanometer sizes are responsible for reserving hydrocarbons. By understanding the structure of porous media and examining fluid flow inside these nanometer pores, we can better understand the porous media›s behaviour on larger scales. Investigating fluid flow in reservoir rocks requires three-dimensional structures with appropriate accuracy. However, using conventional methods to reconstruct a porous medium is expensive. On the other hand, as these structures become more complex, the ability of these methods to reconstruct pore network models decreases significantly. In recent years, with the advance in computer science, especially artificial intelligence, a new gate has been opened for reconstructing complex structures such as tight reservoir rocks. By implementing machine learning methods, three-dimensional pore-scale models can be created with high accuracy. The petrophysical properties of rocks can be calculated from them. One of these methods is the generative adversarial network (GAN), which has proven to reconstruct the pore structure of rocks. This study uses a GAN with convolutional layers to reconstruct the images obtained from FIB-SEM of a tight reservoir rock at the pore scale. Different realizations of the pore space are reconstructed by the trained GAN. The porosity and permeability of the reconstructed images are very close to the properties in the actual FIB-SEM image and have a deviation of 1.07% and 5.24%, respectively. It can be seen that GANs have a high capacity in rock reconstruction at the pore scale, especially for tight reservoirs.