A computer-implemented method for deriving properties of a porous material, the method includes: a first stage including: obtaining a first image of the porous material on a first scale; extracting a first network of pores from the first image; and deriving a first set of properties of the porous material using a first network flow modeling based on the first network; and a second stage including: obtaining a second image of the porous material on a second scale larger than the first scale; extracting a second network of pores from the second image; and deriving a second set of properties of the porous material using a second network flow modeling based on the second network and the first set of properties.

Some aspects of what is described here relate to analyzing a porous rock sample. A suspension of nanoparticles is injected into a rock sample. The rock sample includes pore space defined within a rock medium. An image of the nanoparticles in the pore space is obtained, for example, by three-dimensional x-ray tomography or another non-destructive imaging technique. The pore space of the rock sample is analyzed based on the image. For example, the porosity and permeability of the rock sample can be computed in some cases.

A method for building a 3D model of a rock sample comprises performing X-ray micro/nanoCT scanning of a rock sample and obtaining its initial three-dimensional microstructure image in a gray scale. Then, an analysis of the obtained three-dimensional image of the rock sample is performed and a binarization method is selected in dependence of the image quality and properties of the rock sample. The selected binarization method is at least once applied to the obtained initial three-dimensional image of the sample. Obtained 3D binarized image represents a 3D model of the rock sample.

The present invention provides a method for estimating fluid saturation of a hydrocarbon-bearing rock from a rock image. The image is segmented to represent either a pore space or solid material in the rock. An image pore volume is estimated from the segmented image, and a corrected pore volume is determined to account for the sub-resolution pore volume missing in the image of the rock. An image-derived wetting fluid saturation of the rock is estimated using a direct flow simulation on the rock image and corrected for the corrected pore volume. A backpropagation-enabled trained model can be used to segment the image. A backpropagation-enabled method can be used to estimate the fluid saturation using an image selected from a series of 2D projection images, 3D reconstructed images and combinations thereof.

The present invention provides a method for estimating hydrocarbon saturation of a hydrocarbon-bearing rock from a measurement for an electrical property a resistivity log and a rock image. The image is segmented to represent either a pore space or solid material in the rock. An image porosity is estimated from the segmented image, and a corrected porosity is determined to account for the sub-resolution porosity missing in the image of the rock. A corrected saturation exponent of the rock is determined from the image porosity and the corrected porosity and is used to estimate the hydrocarbon saturation. A backpropagation-enabled trained model can be used to segment the image. A backpropagation-enabled method can be used to estimate the hydrocarbon saturation using an image selected from a series of 2D projection images, 3D reconstructed images and combinations thereof.

Systems and methods are disclosed relating to reservoir characterization. A computed tomography (CT) imaging device is used to generate a CT image of a rock sample from a reservoir and segmented into CT slices. The CT slices are processed to identify textures of the rock sample to provide texture data. The rock sample is scanned using nuclear magnetic resonance (NMR) to provide NMR data. The NMR data is segmented to provide NMR segments. The NMR segments and texture data are analyzed to determine a contribution of each texture in each CT slice to one or more relaxation times in a corresponding NMR segment for each CT slice. A petrophysical property is predicted for each texture of each CT slice based on a contribution of each texture and the corresponding NMR segment for each CT slice. A petrophysical model for the reservoir is generated based on the predicted petrophysical property.

A method of analyzing the pore distribution of porous structures includes observing a cross-section of a porous structure using scanning electron microscopy (SEM) to obtain a raw image of the cross-section of the porous structure and quantifying a pore distribution of the obtained raw image through Voronoi diagram.