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

Author(s):  
Martin Balcewicz ◽  
Mirko Siegert ◽  
Marcel Gurris ◽  
David Krach ◽  
Matthias Ruf ◽  
...  

<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>

2021 ◽  
Vol 9 ◽  
Author(s):  
Martin Balcewicz ◽  
Mirko Siegert ◽  
Marcel Gurris ◽  
Matthias Ruf ◽  
David Krach ◽  
...  

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.


2018 ◽  
Vol 37 (6) ◽  
pp. 428-434
Author(s):  
Sander Hunter ◽  
Ronny Hofmann ◽  
Irene Espejo

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.


2022 ◽  
Author(s):  
Omar Alfarisi ◽  
Djamel Ouzzane ◽  
Mohamed Sassi ◽  
TieJun Zhang

<p><a></a>Each grid block in a 3D geological model requires a rock type that represents all physical and chemical properties of that block. The properties that classify rock types are lithology, permeability, and capillary pressure. Scientists and engineers determined these properties using conventional laboratory measurements, which embedded destructive methods to the sample or altered some of its properties (i.e., wettability, permeability, and porosity) because the measurements process includes sample crushing, fluid flow, or fluid saturation. Lately, Digital Rock Physics (DRT) has emerged to quantify these properties from micro-Computerized Tomography (uCT) and Magnetic Resonance Imaging (MRI) images. However, the literature did not attempt rock typing in a wholly digital context. We propose performing Digital Rock Typing (DRT) by: (1) integrating the latest DRP advances in a novel process that honors digital rock properties determination, while; (2) digitalizing the latest rock typing approaches in carbonate, and (3) introducing a novel carbonate rock typing process that utilizes computer vision capabilities to provide more insight about the heterogeneous carbonate rock texture.<br></p>


Author(s):  
Bankim Mahanta ◽  
P.G. Ranjith ◽  
T.N. Singh ◽  
Vikram Vishal ◽  
WenHui Duan ◽  
...  

Geophysics ◽  
2021 ◽  
pp. 1-76
Author(s):  
Jin Hao ◽  
Guoliang Li ◽  
Jiao Su ◽  
Yuan Yuan ◽  
Zhongming Du ◽  
...  

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.


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