scholarly journals Diagenetic Trends of Synthetic Reservoir Sandstone Properties Assessed by Digital Rock Physics

Minerals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 151
Author(s):  
Maria Wetzel ◽  
Thomas Kempka ◽  
Michael Kühn
Keyword(s):  
Elastic Properties ◽  
Pore Space ◽  
Rock Physics ◽  
Mineral Dissolution ◽  
Ct Scans ◽  
Micro Ct ◽  
Digital Rock Physics ◽  
Gravity Driven ◽  
Stress Dependent ◽  
The Impact

Quantifying interactions and dependencies among geometric, hydraulic and mechanical properties of reservoir sandstones is of particular importance for the exploration and utilisation of the geological subsurface and can be assessed by synthetic sandstones comprising the microstructural complexity of natural rocks. In the present study, three highly resolved samples of the Fontainebleau, Berea and Bentheim sandstones are generated by means of a process-based approach, which combines the gravity-driven deposition of irregularly shaped grains and their diagenetic cementation by three different schemes. The resulting evolution in porosity, permeability and rock stiffness is examined and compared to the respective micro-computer tomographic (micro-CT) scans. The grain contact-preferential scheme implies a progressive clogging of small throats and consequently produces considerably less connected and stiffer samples than the two other schemes. By contrast, uniform quartz overgrowth continuously alters the pore space and leads to the lowest elastic properties. The proposed stress-dependent cementation scheme combines both approaches of contact-cement and quartz overgrowth, resulting in granulometric, hydraulic and elastic properties equivalent to those of the respective micro-CT scans, where bulk moduli slightly deviate by 0.8%, 4.9% and 2.5% for the Fontainebleau, Berea and Bentheim sandstone, respectively. The synthetic samples can be further altered to examine the impact of mineral dissolution or precipitation as well as fracturing on various petrophysical correlations, which is of particular relevance for numerous aspects of a sustainable subsurface utilisation.

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.

Digital reconstruction of reservoir sandstones to predict hydraulic and mechanical rock properties

2021 ◽  
Author(s):  
Maria Wetzel ◽  
Thomas Kempka ◽  
Michael Kühn
Keyword(s):  
Numerical Simulations ◽  
Mineral Content ◽  
Practical Importance ◽  
Rock Physics ◽  
Mineral Dissolution ◽  
Rock Properties ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Reservoir Sandstones ◽  
The Impact

<p>Quantifying trends in hydraulic and mechanical properties of reservoir sandstones has a wide practical importance for many applications related to geological subsurface utilization. In that regard, predicting macroscopic rock properties requires detailed information on their microstructure [1]. In order to fundamentally understand the pore-scale processes governing the rock behaviour, digital rock physics represents a powerful and flexible approach to investigate essential rock property relations [2]. This was shown, e.g., for hydraulic effects of anhydrite cement in the Bentheim sandstone in relation to an unsuccessful drilling campaign at the geothermal well Allermöhe, Germany [3]. Rock weakening due to decreasing calcite mineral content was also demonstrated by application of numerical simulations [4]. </p><p>In the present study, a process-based method is used for reconstructing the full 3D microstructure of three typical reservoir reference rocks: the Fontainebleau, Berea and Bentheim sandstones. For that purpose, grains are initially deposited under the influence of gravity and afterwards diagenetically consolidated. The resulting evolution in porosity, permeability and rock stiffness is examined and compared to the respective micro-CT scans of the sandstones. The presented approach enables to efficiently generate synthetic sandstone samples over a broad range of porosities, comprising the microstructural complexity of natural rocks and considering any desired size, sorting and shape of grains. In view of a virtual laboratory, these synthetic samples can be further altered to examine the impact of mineral dissolution and/or precipitation as well as fracturing on various petrophysical correlations, what is of particular relevance for a sustainable exploration and utilisation of the geological subsurface.</p><p>[1] Wetzel M., Kempka T., Kühn M. (2017): Predicting macroscopic elastic rock properties requires detailed information on microstructure. Energy Procedia, 125, 561-570. DOI: 10.1016/j.egypro.2017.08.195 <br>[2] Wetzel M., Kempka T., Kühn M. (2020): Hydraulic and mechanical impacts of pore space alterations within a sandstone quantified by a flow velocity-dependent precipitation approach. Materials, 13, 4, 3100. DOI: 10.3390/ma13143100<br>[3] Wetzel M., Kempka T., Kühn M. (2020): Digital rock physics approach to simulate hydraulic effects of anhydrite cement in Bentheim sandstone. Advances in Geosciences, 54, 33-39. DOI: 10.5194/adgeo-54-33-2020 <br>[4] Wetzel M., Kempka T., Kühn M. (2018): Quantifying rock weakening due to decreasing calcite mineral content by numerical simulations. Materials, 11, 542. DOI: 10.3390/ma11040542 </p>

Multi-Mineral Segmentation of SEM Images Using Deep Learning Techniques

2021 ◽  
Author(s):  
Vladislav Vasilevich Alekseev ◽  
Denis Mihaylovich Orlov ◽  
Dmitry Anatolevich Koroteev
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Pore Space ◽  
Rock Physics ◽  
Sem Images ◽  
Digital Rock Physics ◽  
Learning Techniques ◽  
Digital Core ◽  
Non Destructive

Abstract The approaches of building and methods of using the digital core are currently developing rapidly. The use of these methods makes it possible to obtain petrophysical information by non-destructive methods quickly. Digital rock physics includes two main stages: constructing models and modeling various physical processes on the obtained models. Our work proposes using deep learning methods for mineral and pore space segmentation instead of classical methods such as threshold image processing. Deep neural networks have long been able to show their advantages in many areas of computer vision. This paper proposes and tests methods that help identify different minerals in images from a scanning electron microscope. We used images of rocks of the Achimov formation, which are arkoses, as samples. We tested various deep neural networks such as LinkNet, U-Net, ResUNet, and pix2pix and identified those that performed best in segmentation.

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

Numerical method for 3D two-component isothermal compressible flows with application to digital rock physics

2019 ◽  
Vol 34 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Vladislav Balashov ◽  
Evgenii Savenkov ◽  
Alexander Zlotnik
Keyword(s):  
Compressible Flows ◽  
Pore Space ◽  
Rock Physics ◽  
Navier Stokes ◽  
Droplet Spreading ◽  
Good Efficiency ◽  
Digital Rock Physics ◽  
Flat Base ◽  
Two Component ◽  
Voxel Representation

Abstract We propose a numerical algorithm for simulations of two-component viscous compressible isothermal flows with surface effects in 3D domains of complex shape with voxel representation of geometry. The basic mathematical model is the regularized system of Navier–Stokes–Cahn–Hilliard equations. Simulations of droplet spreading over a flat base and displacement of one liquid by another one in a pore space of real rock sample are carried out. The simulation results demonstrate the applicability and good efficiency of the used system of equations, the corresponding difference scheme, and its implementation algorithms for numerical solution of the considered class of problems.

scholarly journals A digital rock physics approach to effective and total porosity for complex carbonates: pore-typing and applications to electrical conductivity

2019 ◽  
Vol 89 ◽  
pp. 05002 ◽  
Author(s):  
Christoph H. Arns ◽  
Han Jiang ◽  
Hongyi Dai ◽  
Igor Shikhov ◽  
Nawaf Sayedakram ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Multiple Scales ◽  
Rock Physics ◽  
Total Porosity ◽  
Structural Heterogeneity ◽  
Micro Ct ◽  
Digital Rock Physics ◽  
Consistent Manner ◽  
The Face ◽  
Pore Types

Recent advances in micro-CT techniques allow imaging heterogeneous carbonates at multiple scales and including voxel-wise registration of images at different resolution or in different saturation states. This enables characterising such carbonates at the pore-scale targeting the optimizing of hydrocarbon recovery in the face of structural heterogeneity, resulting in complex spatial fluid distributions. Here we determine effective and total porosity for different pore-types in a complex carbonate and apply this knowledge to improve our understanding of electrical properties by integrating experiment and simulation in a consistent manner via integrated core analysis. We consider Indiana Limestone as a surrogate for complex carbonate rock and type porosity in terms of macro- and micro-porosity using micro-CT images recorded at different resolution. Effective and total porosity fields are derived and partitioned into regions of macro-porosity, micro-porosity belonging to oolithes, and micro-porosity excluding oolithes’ rims. In a second step we use the partitioning of the micro-porosity to model the electrical conductivity of the limestone, matching experimental measurements by finding appropriate cementation exponents for the two different micro-porosity regions. We compare these calculations with calculations using a single cementation exponent for the full micro-porosity range. The comparison is extended to resistivity index at partial saturation, further testing the assignment of Archie parameters, providing insights into the regional connectivity of the different pore types.

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.

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