--> Reconstruction of 3-D Pore Space Using Multiple-Point Statistics Based on a 2-D Training Image

AAPG ACE 2018

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Reconstruction of 3-D Pore Space Using Multiple-Point Statistics Based on a 2-D Training Image

Abstract

Macroscopic transport properties of porous media essentially rely on the geometry and topology of their pore space. The premise of predicting these transport properties is to construct an accurate 3D pore space. So far, the methods of modeling porous media are divided into two main groups, equipment imaging and stochastic statistical methods. The former method can acquire pore structure imaging using modern equipment such as X-ray computed tomography and laser scanning confocal microscopy, but the unavailability and the high cost of the equipment make their widespread application impossible. The current latter methods, such as truncated Gaussian random field and simulated annealing methods, reconstruct 3D porous media based on some 2D thin sections by means of lower-order statistical functions. However these functions cannot be able to reproduce the long-range connectivity of pore structure. Therefore, our research will present a stochastic technique of reconstructing 3D pore space using multiple-point statistics with the purpose of solving the proposed problems. The single normal equation simulation algorithm that serves as the simulation engine is the main tool to reproduce the long-range feature of pore space. In the simulation process, we took a 2D thin section as the training image for providing patterns of pore structure and extracted some pixels from it as the conditioning data. The extracted pixels must come from the centered region of a grain or pore, which can ensure the continuity of grain or pore among the adjacent layers to be simulated. To test the accuracy of the method, Berea sandstone was used to test the method. Pore geometry and topology and transport properties of the reconstructed models were compared with them of the real model obtained by X-ray computed tomography scanning. The comparison result shows that reconstructed models are good agreement with the real model obtained by X-ray computed tomography scanning in the two-point correlation function, the pore and throat size distributions, and single- and two-phase flow permeabilities, which verifies that the long-range connectivity of pore space can be reproduced by this method. Compared with other stochastic methods, a more accurate stochastic method of reconstructing 3D porous media is presented when only some 2D thin sections are available.