Multiscale study for stochastic characterization of shale samples. (March 2016) - Tahmasebi, Pejman; Javadpour, Farzam; Sahimi, Muhammad; Piri, Mohammad
Highlights: An algorithm for accurate modeling of pore-space in shale reservoirs. Various multiscale, multiresolution and histogram matching algorithms are combined. Images from diverse resolutions are integrated. Abstract: Characterization of shale reservoirs, which are typically of low permeability, is very difficult because of the presence of multiscale structures. While three-dimensional (3D) imaging can be an ultimate solution for revealing important complexities of such reservoirs, acquiring such images is costly and time consuming. On the other hand, high-quality 2D images, which are widely available, also reveal useful information about shales' pore connectivity and size. Most of the current modeling methods that are based on 2D images use limited and insufficient extracted information. One remedy to the shortcoming is direct use of qualitative images, a concept that we introduce in this paper. We demonstrate that higher-order statistics (as opposed to the traditional two-point statistics, such as variograms) are necessary for developing an accurate model of shales, and describe an efficient method for using 2D images that is capable of utilizing qualitative and physical information within an image and generating stochastic realizations of shales. We then further refine the model by describing and utilizing several techniques, including an iterative framework, for removing some possible artifacts and better pattern reproduction. Next, we introduce a new histogram-matching algorithm that accounts for concealed nanostructures in shale samples. We also present two new multiresolution and multiscale approaches for dealing with distinct pore structures that are common in shale reservoirs. In the multiresolution method, the original high-quality image is upscaled in a pyramid-like manner in order to achieve more accurate global and long-range structures. The multiscale approach integrates two images, each containing diverse pore networks – the nano- and microscale pores – using a high-resolution image representing small-scale pores and, at the same time, reconstructing large pores using a low-quality image. Eventually, the results are integrated to generate a 3D model. The methods are tested on two shale samples for which full 3D samples are available. The quantitative accuracy of the models is demonstrated by computing their morphological and flow properties and comparing them with those of the actual 3D images. The success of the method hinges upon the use of very different low- and high-resolution images. Graphical abstract: