scholarly journals Digital Rock Physics: A Geological Driven Workflow for the Segmentation of Anisotropic Ruhr Sandstone

2021 ◽  
Vol 9 ◽  
Author(s):  
Martin Balcewicz ◽  
Mirko Siegert ◽  
Marcel Gurris ◽  
Matthias Ruf ◽  
David Krach ◽  
...  
Keyword(s):  
High Resolution ◽  
Rock Physics ◽  
Homogeneity Test ◽  
Sample Sizes ◽  
Gray Scale ◽  
Digital Rock Physics ◽  
Digital Twin ◽  
Digital Rock ◽  
Gray Scale Image

Over the last 3 decades, Digital Rock Physics (DRP) has become a complementary part of the characterization of reservoir rocks due to the non-destructive testing character of this technique. The use of high-resolution X-ray Computed Tomography (XRCT) has become widely accepted to create a digital twin of the material under investigation. Compared to other imaging techniques, XRCT technology allows a location-dependent resolution of the individual material particles in volume. However, there are still challenges in assigning physical properties to a particular voxel within the digital twin, due to standard histogram analysis or sub-resolution features in the rock. For this reason, high-resolution image-based data from XRCT, transmitted-light microscope, Scanning Electron Microscope (SEM) as well as geological input properties like geological diagenesis, mineralogical composition, sample’s microfabrics, and estimated sample’s porosity are combined to obtain an optimal spatial segmented image of the studied Ruhr sandstone. Based on a homogeneity test, which corresponds to the evaluation of the gray-scale image histogram, the preferred scan sample sizes in terms of permeability, thermal, and effective elastic rock properties are determined. In addition, these numerically derived property predictions are compared with laboratory measurements to obtain possible upper limits for sample size, segmentation accuracy, and a geometrically calibrated digital twin of the Ruhr sandstone. The comparison corresponding gray-scale image histograms as a function of sample sizes with the corresponding advanced numerical simulations provides a unique workflow for reservoir characterization of the Ruhr sandstone.

Digital rock physics: A geological driven segmentation workflow for Ruhr sandstone

2021 ◽  
Author(s):  
Martin Balcewicz ◽  
Mirko Siegert ◽  
Marcel Gurris ◽  
David Krach ◽  
Matthias Ruf ◽  
...  
Keyword(s):  
High Resolution ◽  
Rock Physics ◽  
Rock Properties ◽  
Homogeneity Test ◽  
Grayscale Image ◽  
Sample Sizes ◽  
Digital Rock Physics ◽  
Digital Twin ◽  
Digital Rock

<p>Over the last two decades, Digital Rock Physics (DRP) has become a complementary part of the characterization of reservoir rocks due to, among other things, the non-destructive testing character of this technique. The use of high-resolution X-ray Computed Tomography (XRCT) has become widely accepted to create a digital twin of the material under investigation. Compared to other imaging techniques, XRCT technology allows a location-dependent resolution of the individual material particles in volume. However, there are still challenges in assigning physical properties to a particular voxel within the digital twin, due to standard histogram analysis or sub-resolution features in the rock. For this reason, high-resolution image-based data from XRCT, transmitted-light microscope, Scanning Electron Microscope (SEM) as well as inherent material properties like porosity are combined to obtain an optimal spatial image of the studied Ruhr sandstone by a geologically driven segmentation workflow. On the basis of a homogeneity test, which corresponds to the evaluation of the grayscale image histogram, the preferred scan sample sizes in terms of transport, thermal, and effective elastic rock properties are determined. In addition, the advanced numerical simulation results are compared with laboratory tests to provide possible upper limits for sample size, segmentation accuracy, and a calibrated digital twin of the Ruhr sandstone. The comparison of representative grayscale image histograms as a function of sample sizes with the corresponding advanced numerical simulations, provides a unique workflow for reservoir characterization of the Ruhr sandstone.</p>

Digital rock physics and application of high-resolution micro-CT techniques for geomaterials

2018 ◽  
pp. 299-307 ◽  
Author(s):  
Bankim Mahanta ◽  
P.G. Ranjith ◽  
T.N. Singh ◽  
Vikram Vishal ◽  
WenHui Duan ◽  
...  
Keyword(s):  
High Resolution ◽  
Rock Physics ◽  
Micro Ct ◽  
Digital Rock Physics ◽  
Digital Rock

3D rock minerals by correlating XRM and automated mineralogy and its application to digital rock physics for elastic properties

Geophysics ◽  
2021 ◽  
pp. 1-76
Author(s):  
Jin Hao ◽  
Guoliang Li ◽  
Jiao Su ◽  
Yuan Yuan ◽  
Zhongming Du ◽  
...  
Keyword(s):  
High Resolution ◽  
Elastic Properties ◽  
Rock Physics ◽  
Large Field ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Mineral Phases ◽  
Rock Structure ◽  
Shale Sample ◽  
Automated Mineralogy

Digital rock physics (DRP) is an emerging technique that has rapidly become an indispensable tool to estimate elastic properties. The success of DRP mainly depends on three factors: acquiring a 3D rock structure image, accurately identifying 3D minerals, and using a proper numerical simulation method. Shales present a substantial challenge for DRP owing to their heterogeneous structure, composition, and properties from micron to centimeter scale. To obtain a sufficiently large field-of-view (FOV) image of a sample that reflects the detailed and representative internal structure and composition, we have developed a new DRP workflow to obtain large-FOV high-resolution digital rocks with 3D mineralogical information. Using the “divide-and-stitch” technique, a long shale sample is divided into several subunits, imaged separately by high-resolution X-ray microscopy (XRM), and then stitched to obtain a large-FOV 3D digital rock. An FOV of a rock cylinder (736 μm in diameter, 2358 μm in height, and 1 μm resolution) is used as an example. By correlating XRM and automated mineralogy, a large-FOV 3D mineral digital rock is obtained from a shale sample. Six mineral phases are identified and verified by automated mineralogy, and four laminae are detected according to the grain size, which offer a new perspective to study sedimentary processes and heterogeneities at the millimeter scale. The finite-difference method is used to compute the elastic properties of the large-FOV 3D mineral digital rock, and the results of Young’s modulus are within the limit of the Voigt/Reuss bounds. It also reveals that there is a difference in simulated elastic properties in the four laminae. The large-FOV 3D mineral digital rock offers the potential to explore the relationship between elastic properties and mineral phases, as well as the heterogeneities of elastic properties at the millimeter scale.

Integrating grain-scale geology in digital rock physics

2018 ◽  
Vol 37 (6) ◽  
pp. 428-434
Author(s):  
Sander Hunter ◽  
Ronny Hofmann ◽  
Irene Espejo
Keyword(s):  
Pore Space ◽  
Digital Simulation ◽  
Rock Physics ◽  
Rock Properties ◽  
Micro Ct ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Full Characterization ◽  
Compositional Information

Digital rock physics (DRP) is a rapidly evolving field of study. One component of digital rock that has not received sufficient attention is how well actual rocks are represented in DRP. Instead, the digital rock community is focused on characterizing the pore space in volumes of rock imaged by microcomputed tomography (micro-CT) and simulating flow through that digitized pore network. This enables computational simulations of routine core analysis measurements, which may be completed in hours instead of days or weeks. Although this alone makes digital rock a worthwhile endeavor, it overlooks much of the detailed textural and compositional information stored within digital rock images below the resolution of micro-CT imaging. This information may be observed in high-resolution 2D transmitted light microscopy images. Textural information impacts not only the tortuosity of the flow path, impacting permeability, but also influences how the rock will respond to stress. Compositional information could also be extracted to not only better characterize the wettability of rocks for relative permeability simulations, but also to supplement petrographic information in diagenetic modeling, among other applications. Ultimately, a full characterization of a digital rock should replicate the acoustic, geomechanical, and petrophysical properties of the imaged sample. The first step toward achieving full digital simulation of rock properties is the fundamental characterization of the sample — extracting the textural and compositional information from digital rock images. Unfortunately, this is a nontrivial undertaking. It involves acquiring sample images, segmenting pores from individual rock minerals, separating these minerals into individual grains and cements, and computing multiple attributes from the segmented grains. To address this issue, we are developing a workflow to compute key textural attributes from images with a long-term vision for the incorporation of geologic characterization into DRP using machine learning.

scholarly journals Estimation Microporosity Value of Fontanebleau Sandstone Using Digital Rock Physics Approach

2018 ◽  
Vol 3 (2) ◽  
pp. 103
Author(s):  
Reza Rizki ◽  
Handoyo Handoyo
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
Physical Properties ◽  
Rock Physics ◽  
3D Image ◽  
Sandstone Sample ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Micro Tomography ◽  
2D And 3D

The technology of digital rock physics (DRP) allowed to predict the physical properties in core data sample, for example to predict value of porosity of data sample. This research applied the digital rock physics technique to predict the microporosity in sandstone sample: Fontanebleau Sandstone. The data are digital images from Fontanebleau Sandstone with high resolution scanned from micro tomography CT-Scan processing. The result of image processing shown in 2D and 3D image. From the data, the value of microporosity Fontanebleau Sandstone are beetwen 6% - 7%. This result confirmed by the quartz cemented sample of Fontanebleau Sandstone. The scale and sub-cube give the different value of microporosity which is indicated the scale influence to value of porosity value. So the simplest and best way is to average the all result from sub-cubes.

scholarly journals Conditional reconstruction: An alternative strategy in digital rock physics

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. D465-D477 ◽  
Author(s):  
Sadegh Karimpouli ◽  
Pejman Tahmasebi
Keyword(s):  
High Resolution ◽  
Elastic Moduli ◽  
Rock Physics ◽  
Image Resolution ◽  
Work Flow ◽  
Physical Parameters ◽  
Reconstruction Algorithms ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Carbonate Formation

Digital rock physics (DRP) is a newly developed method based on imaging and digitizing of 3D pore and mineral structure of actual rock and numerically computing rock physical properties, such as permeability, elastic moduli, and formation factor. Modern high-resolution microcomputed tomography scanners are used for imaging, but these devices are not widely available, and 3D imaging is also costly and it is a time-consuming procedure. However, recent improvements of 3D reconstruction algorithms such as crosscorrelation-based simulation and, on the other side, the concept of rock physical trends have provided some new avenues in DRP. We have developed a modified work flow using higher order statistical methods. First, a high-resolution 2D image is divided into smaller subimages. Then, different stochastic subsamples are generated based on the provided 2D subimages. Eventually, various rock physical parameters are calculated. Using several subsamples allows extracting rock physical trends and better capturing the heterogeneity and variability. We implemented our work flow on two DRP benchmark data (Berea sandstone and Grosmont carbonate) and a thin-section image from the Grosmont carbonate formation. Results of realization models, pore network modeling, and autocorrelation functions for the real and reconstructed subsamples reveal the validity of the reconstructed models. Furthermore, the agreement between static and dynamic methods indicates that subsamples are representative volume elements. Average values of the subsamples’ properties follow the reference trends of the rock sample. Permeability trends pass the actual results of the benchmark samples; however, elastic moduli trends find higher values. The latter can be due to image resolution and voxel size, which are generated by imaging tools and reconstruction algorithms. According to the obtained results, this strategy can be introduced as a valid and accurate method where an alternative method for standard DRP is needed.

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