8 Works

Evaluation of Capillary Pressure in Digital Rock Petrophysics

Christoph Arns, , , &

Bentheimer Sandstone for Analyzing Wetting Phenomena

Chenhao Sun, James McClure, Peyman Mostaghimi, Anna Herring, Steffen Berg & Ryan Armstrong
The micro-CT image data of Bentheimer sandstone used in characterizing its wettability. The primary drainage and imbibition experiments were performed by using air and brine. The images were acquired at irreducible air saturation Sw=94%. This dataset is used to characterize wetting in complex subsurface multiphase systems by using principles of topology and integral geometry.

Unpaired super-resolution on micro-CT sandstone by using cycle-consistent generative adversarial network

Yufu Niu, Ryan Armstrong & Peyman Mostaghimi
High-resolution X-ray micro-computed tomography (CT) data is required for accurate determination of rock petrophysical properties. High-resolution data, however, results in small field-of-view, thus the representativeness of simulation domain can be brought into question for geophysical applications. This project aims to develop new techniques for super resolution in digital rock.

Grayscale REV Analysis

Ankita Singh, Peyman Mostaghimi, Klaus Regenauer-lieb, Ryan Armstrong & Stuart Walsh
The data provided here is used to investigate the representative elementary volume (REV) size directly from grayscale micro-CT images of porous media of sandstone and carbonate. Three datasets are provided per sample: 16-bit grayscale image, 8-bit grayscale image, and segmented image. The re-quantization of a 16-bit grayscale image to 8-bit is carried out using ImageJ. The choice of intensities used carrying out this operation is provided in the supplementary information of the related publication. This...

Micro-CT scans of Mt. Simon sandstone at residual conditions used for contact angle measurements and analyzing the influence of clay regions

Laura Dalton, Dustin Crandall, James McClure, Min Fang & Ryan Armstrong
Micro-computed tomography tiff stacks of a Mt. Simon core at residual conditions. The core is from a depth of 6,926 ft, the sub-sample tested is 0.25 inch diameter by 1 inch length. This data was used to complete contact angle measurements and analyze the influence of clay-rich regions in the pore space on residual trapping efficiency.

Pore scale in situ imaging of multiphase flow at steady-state for an altered mixed-wet Bentheimer sandstone

Shuangemei Zou, Yang Liu, Jianchao Cai & Ryan Armstrong
The data sets contains in situ imaged fluid distribitions for two-phase drainage at four fractional flows under steady state condition for a water-wet Bentheimer sandstone and an altered mixed-wet Bentheimer sandstone. These multiphase images are used for curvature measurement and relative permeability measurement for two wettability conditions while dry images are used for porosity and absolute permeability measurements. Further details on experimental protocols can be found in Zou (2018,2019). Data was acquired at the University...

A Diverse Super Resolution Dataset of Digital Rocks (DeepRock-SR): Sandstone, Carbonate, and Coal

Ying Da Wang, Ryan Armstrong & Peyman Mostaghimi
A Diverse Super Resolution Dataset of Digital Rocks (DeepRockSR): Sandstone, Carbonate, and Coal. This dataset contains an organised and processed collection of greyscale digital rock images for the purpose of image super resolution training. In total, there are 12,000 2D images and 3,000 3D volumes contained in this dataset. Sandstone: Bentheimer Sandstone 1 https://www.digitalrocksportal.org/projects/211 Bentheimer Sandstone 2 https://www.digitalrocksportal.org/projects/135 Berea Sandstone https://www.digitalrocksportal.org/projects/135 Leopard Sandstone https://www.digitalrocksportal.org/projects/135 Gildehauser Sandstone https://www.digitalrocksportal.org/projects/134 Wilcox Tight Sandstone https://www.digitalrocksportal.org/projects/6 Carbonate: Estaillades Carbonate https://www.digitalrocksportal.org/projects/58...

Digital rock segmentation from micro-CT/SEM data by using convolutional neural network

Pore-scale digital images are usually obtained from micro computed tomography (micro-CT) data that has been segmented into void and grain space. Image segmentation is a critical process for digital rock analyses that can influence pore-scale characterisation studies and/or the numerical simulation of petrophysical properties. This project is to study on the sandstone micro-CT image segmentation by using convolutional neural network.

Registration Year

  • 2020
  • 2019

Resource Types

  • Dataset


  • UNSW Sydney
  • Virginia Tech
  • Shell (Netherlands)
  • The University of Texas at Austin
  • Australian National University
  • National Energy Technology Laboratory
  • China University of Geosciences