GPU-accelerated 3D reconstruction of porous media using multiple-point statistics

Abstract

It is very important for the study of predicting fluid transport properties or mechanisms of fluid flow in porous media that the characteristics of porous media can be extracted in relatively smaller scales and then are copied in a larger or even arbitrary region to reconstruct virtual 3D porous media that have similar structures with the real porous media. One of multiple-point statistics (MPS) method, the single normal equation simulation algorithm (SNESIM), has been widely used in reconstructing 3D porous media recently. However, owing to its large CPU cost and rigid memory demand, the application of SNESIM has been limited in some cases. To overcome this disadvantage, parallelization of SNESIM is performed on the compute unified device architecture (CUDA) kernels in the graphic processing unit (GPU) to reconstruct each node on simulation grids, combined with choosing the optimal size of data templates based on the entropy calculation towards the training image (TI) to acquire high-quality reconstruction with a low CPU cost; meanwhile, the integration of hard data and soft data is also included in the processing of CUDA kernels to improve the accuracy. Representative elementary volumes (REVs) for porosity, variogram, and entropy are analyzed to guarantee that the scale of observation is large enough and parameters of concern are constant. This parallel GPU-version 3D porous media reconstruction only requires relatively small size memory and benefits from the tremendous calculating power given by CUDA kernels to shorten the CPU time, showing its high efficiency for the reconstruction of porous media.